{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5ea29b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>套餐名词</th>\n",
       "      <th>鸡腿</th>\n",
       "      <th>翅中</th>\n",
       "      <th>翅根</th>\n",
       "      <th>无骨鸡</th>\n",
       "      <th>年糕</th>\n",
       "      <th>芝士年糕</th>\n",
       "      <th>地瓜块</th>\n",
       "      <th>雪碧300ml</th>\n",
       "      <th>实付</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>炸鸡套餐买一送一</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>29.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>无骨口味双拼-尝鲜套餐</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>23.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>大胃王套餐</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>年糕爱上鸡</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>单人柠檬炸鸡套餐a1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>单人柠檬炸鸡套餐a2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>单人柠檬炸鸡套餐a3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>单人柠檬炸鸡套餐a4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>单人柠檬炸鸡套餐b1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>单人柠檬炸鸡套餐b2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>单人柠檬炸鸡套餐b3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>单人柠檬炸鸡套餐b4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2翅根5无骨鸡6年糕单人餐</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>不买</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>朋友开黑套餐+2饮料</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>52.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>欢迎学子归家套餐</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>炸鸡套餐买一送一+汽水</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>34.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>秘制大鸡腿*5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>34.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>秘制大鸡腿*8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>53.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>秘制大鸡腿 10个双拼</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>68.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>欢迎学子归家套餐+地瓜</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>29.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             套餐名词  鸡腿  翅中  翅根  无骨鸡  年糕  芝士年糕  地瓜块  雪碧300ml     实付\n",
       "0        炸鸡套餐买一送一   2   0   2    6   6     0    6        0  29.00\n",
       "1     无骨口味双拼-尝鲜套餐   0   0   0    8   6     0    4        0  23.80\n",
       "2           大胃王套餐   0   0   2    5   5     5    0        0  24.80\n",
       "3           年糕爱上鸡   0   0   0   10   5     5    0        0  24.80\n",
       "4      单人柠檬炸鸡套餐a1   0   0   2    6   0     0    4        1  25.80\n",
       "5      单人柠檬炸鸡套餐a2   0   0   2    6   4     0    0        1  25.80\n",
       "6      单人柠檬炸鸡套餐a3   0   0   2    6   0     3    0        1  25.80\n",
       "7      单人柠檬炸鸡套餐a4   0   0   2    6   2     0    2        1  25.80\n",
       "8      单人柠檬炸鸡套餐b1   1   0   0    5   0     0    4        1  25.80\n",
       "9      单人柠檬炸鸡套餐b2   1   0   0    5   4     0    0        1  25.80\n",
       "10     单人柠檬炸鸡套餐b3   1   0   0    5   0     3    0        1  25.80\n",
       "11     单人柠檬炸鸡套餐b4   1   0   0    5   2     0    2        1  25.80\n",
       "12  2翅根5无骨鸡6年糕单人餐   0   0   2    5   6     0    0        0  26.80\n",
       "13             不买   0   0   0    0   0     0    0        0   0.00\n",
       "14     朋友开黑套餐+2饮料   2   2   2   10   4     4    6        2  52.80\n",
       "15       欢迎学子归家套餐   1   0   2    4   2     0    2        1  26.80\n",
       "16    炸鸡套餐买一送一+汽水   2   0   2    6   6     0    6        1  34.90\n",
       "17        秘制大鸡腿*5   5   0   0    0   6     0    4        1  34.80\n",
       "18        秘制大鸡腿*8   8   0   0    0   8     0    4        2  53.80\n",
       "19    秘制大鸡腿 10个双拼  10   0   0    0  10     0    6        2  68.80\n",
       "20    欢迎学子归家套餐+地瓜   1   0   2    4   2     0    8        1  29.79"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "data = pandas.read_csv(\n",
    "    '炸鸡.csv', encoding='gb18030', sep=\",\"\n",
    ")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abbca20a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>鸡腿</th>\n",
       "      <th>翅中</th>\n",
       "      <th>翅根</th>\n",
       "      <th>无骨鸡</th>\n",
       "      <th>年糕</th>\n",
       "      <th>芝士年糕</th>\n",
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       "      <th>雪碧300ml</th>\n",
       "      <th>实付</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>鸡腿</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028013</td>\n",
       "      <td>-0.370294</td>\n",
       "      <td>-0.622758</td>\n",
       "      <td>0.670155</td>\n",
       "      <td>-0.227274</td>\n",
       "      <td>0.467041</td>\n",
       "      <td>0.644309</td>\n",
       "      <td>0.830401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>翅中</th>\n",
       "      <td>0.028013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213201</td>\n",
       "      <td>0.408318</td>\n",
       "      <td>0.022569</td>\n",
       "      <td>0.387488</td>\n",
       "      <td>0.280618</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.373651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>翅根</th>\n",
       "      <td>-0.370294</td>\n",
       "      <td>0.213201</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.357888</td>\n",
       "      <td>-0.129914</td>\n",
       "      <td>0.082613</td>\n",
       "      <td>0.133733</td>\n",
       "      <td>-0.063960</td>\n",
       "      <td>-0.040650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无骨鸡</th>\n",
       "      <td>-0.622758</td>\n",
       "      <td>0.408318</td>\n",
       "      <td>0.357888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190276</td>\n",
       "      <td>0.488938</td>\n",
       "      <td>-0.076762</td>\n",
       "      <td>-0.249528</td>\n",
       "      <td>-0.158613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年糕</th>\n",
       "      <td>0.670155</td>\n",
       "      <td>0.022569</td>\n",
       "      <td>-0.129914</td>\n",
       "      <td>-0.190276</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079252</td>\n",
       "      <td>0.316657</td>\n",
       "      <td>0.135411</td>\n",
       "      <td>0.662394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芝士年糕</th>\n",
       "      <td>-0.227274</td>\n",
       "      <td>0.387488</td>\n",
       "      <td>0.082613</td>\n",
       "      <td>0.488938</td>\n",
       "      <td>-0.079252</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.327807</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>0.014221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地瓜块</th>\n",
       "      <td>0.467041</td>\n",
       "      <td>0.280618</td>\n",
       "      <td>0.133733</td>\n",
       "      <td>-0.076762</td>\n",
       "      <td>0.316657</td>\n",
       "      <td>-0.327807</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412674</td>\n",
       "      <td>0.554878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雪碧300ml</th>\n",
       "      <td>0.644309</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.063960</td>\n",
       "      <td>-0.249528</td>\n",
       "      <td>0.135411</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>0.412674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>实付</th>\n",
       "      <td>0.830401</td>\n",
       "      <td>0.373651</td>\n",
       "      <td>-0.040650</td>\n",
       "      <td>-0.158613</td>\n",
       "      <td>0.662394</td>\n",
       "      <td>0.014221</td>\n",
       "      <td>0.554878</td>\n",
       "      <td>0.761305</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               鸡腿        翅中        翅根       无骨鸡        年糕      芝士年糕       地瓜块  \\\n",
       "鸡腿       1.000000  0.028013 -0.370294 -0.622758  0.670155 -0.227274  0.467041   \n",
       "翅中       0.028013  1.000000  0.213201  0.408318  0.022569  0.387488  0.280618   \n",
       "翅根      -0.370294  0.213201  1.000000  0.357888 -0.129914  0.082613  0.133733   \n",
       "无骨鸡     -0.622758  0.408318  0.357888  1.000000 -0.190276  0.488938 -0.076762   \n",
       "年糕       0.670155  0.022569 -0.129914 -0.190276  1.000000 -0.079252  0.316657   \n",
       "芝士年糕    -0.227274  0.387488  0.082613  0.488938 -0.079252  1.000000 -0.327807   \n",
       "地瓜块      0.467041  0.280618  0.133733 -0.076762  0.316657 -0.327807  1.000000   \n",
       "雪碧300ml  0.644309  0.400000 -0.063960 -0.249528  0.135411 -0.133199  0.412674   \n",
       "实付       0.830401  0.373651 -0.040650 -0.158613  0.662394  0.014221  0.554878   \n",
       "\n",
       "          雪碧300ml        实付  \n",
       "鸡腿       0.644309  0.830401  \n",
       "翅中       0.400000  0.373651  \n",
       "翅根      -0.063960 -0.040650  \n",
       "无骨鸡     -0.249528 -0.158613  \n",
       "年糕       0.135411  0.662394  \n",
       "芝士年糕    -0.133199  0.014221  \n",
       "地瓜块      0.412674  0.554878  \n",
       "雪碧300ml  1.000000  0.761305  \n",
       "实付       0.761305  1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7fMb3uXjbe7wTvS59qH0woXI7B7160GPuZ+3xB3Vrv1uHQmHD+3c0v9lYr5/a15aSsrXWhtW7a8ij//Jygz7Y2qfD4bDuGvLqAZPbdypAnY6S08jdhbNEtsPGp69ZleowTOewWXjThkWpDsMUhTkO1pr0yzraIwleF+eD+pLNalGsWVgU8/rLK/NZHsPjGEbN1l+2TruVD16yNNVhjDHrbrB423u8RcWJ3S0GqRkxdNqtVBWa8+iEaD58+bKUlm+Usjwn77ygZty/Uz8iHCtTThcqpSqVUo8ppe5WShWZUaYQQgghRCqZNZL1BeAWIAt4D/Azk8pNG3tbBqj54sMsLM7m+f+8KtXhmKah28XlP3iWt5y7kE9evSLV4RjG7/dz8a2b6HcHOGdBIf2eAL94tp7LV5Zz9oL4H6CXiLrGXn7+bD0FTjvfvOEsikZG1X7y5GH+8FIjxdl27v63iyjLm3qUY2tDL1sbelg1r4Brzqyccl2tNY/tbedo1zAXLy+Lq729gRD3bG9m2Bfk+jVVLJxmZKNrKHLXnM2iuPHchRSOXCMTrd7PHOzk9y80UFXo5OtvOIsch3GHw/2tgyz/0iOcv6SEv37kQsPKiUV95zCP72unIj+LN65fgN2avN/a4XCY7z56kANtg1y3Zn7C9R72BfnXjma8gRBvWLuAeYWxj7xFa28zHWwf4oz/fpQrz6jg/969wZQy//xSIz968jDZDit/+fAF1JQZ+4DdyST7uFbfOcQPHjvExkMdBLWiuiSbn910rmnHzGQw68L3KmAPsBtYYlKZaWX0RtSWPmMmqZytfMEwwVCYpw+k5o4cs7zSPEivy09Ya/a3DeILhPAHw+w2cR7DZw914fGH6Bj0sv34yVvqH9nbRigUpnvYx9P7p2+HXU39BEKavS0D0z4qwRMIcbB9iEAoMnlzPO3d1Ouma8iHxx/iQAx3Cx7pGGLIG6TPHaBx3Dxq0er97MFO/MEwx3vcHGo39i6lkNaEteaVE/2GlhOLfa0D+INhmvs8dA35krrtXref3c0DBEKa5450JVzv4z0ueob9uHwhDsbw+I7xorW3mYKhMGGteemosY9rGe/ena0EQmEGPQH+uaPFtHLHS/ZxbW/LIIfaBvCFIBTWtPZ7TD1mJoNZSVY+sBF4GjhtXgml1M1KqTqlVF1XV5dJIc1OZk2HMFtk2SxYLYqrzph9T0JPpvULCyjOcaCUYvW8fLJsVmwWNTY1hBkuX1mG026loiCL9YtOPhn6tWfNw2qxUJqXxdVnTt8O5ywsxGZRnDm/YNpRkGy7lZWV+dgsirMXFMbV3guLcyjLz8Jpt3LGvOmvy1pemUdulpXCbPspT4SOVu8rVpXjsFmoLslhVQzbnwmrUiilWLeoyNByYnHW/AIcNgsLirKTfu1SSY6DsxdE9o9LlpcnXO/FpbmU5DrIcVjjbpto7W0mm9WCUooLl5aaVuYb1s7DZrWQ77TzxnXmTEo9UbKPa2fNL2BFVSFZ1kgfqip0mnrMTAYVuTDe4EKU+jPwf8AlwMVa6xuirVtWVqZrampi2m5Y68joQBgKc+xk2dL3iRSNjY3EWu+5JJPq3efy4w+Fyc2y0d3WLPVOMn8oTL87gAKKcx1jjwCYDdJ5Px/yBnD7Q2TZLHGfeku3eg/7grh8QRxWC8UzuIki3eo9E+l+XBttc7vVkvCNM9u3b9da60kTELOuyfo48AvgPODTE19USt0M3AxQXV1NXV1dTBut7xziwV1tAJyzoJBXT3N9yGxWW1sbc73nkkypt9sf5NebIg8ALcl18LP/eIvUO8lerO9mS0MvAFedUcHaWTBqNCqd9/PfbD7GsC8yQ8Anrloe1zVc6VbvP7zQQJ87AMDHLl9GtiOxCYPTrd6JmgvHtT+9fJzukdPmH750CfnO+J9VqZTaEe01U4Z+tNYDwDeBl7TWj07y+u1a61qtdW15eXnM211cmsvS8lzK87Nm1QFViIlyHDZqa4opyXWYegoh1cys91kLCqkqdLKoJIeVcTz2QUztomWlFOfYuXBpaVIvkp+NLlpWRkmug9qa4oQTrEwyF45rFy0toSTXwfrqooQSrOmYMpKllMoBbgU+mMzt2q0WbkjRuWch4nXpinIuXRH7j4i5wqx6F2bbecf51YaXk2nOXlCYVndzzcSqefmGX58316T7cW15RT7LY5hRIlFm/Sz5L6ACuFUp9SaTyhRCCCGESBlTRrK01l8BvmJGWUIIIYQQs8HcPsEuhBBCCJEikmQJIYQQQhhAkiwhhBBCCANIkiWEEEIIYQBJsoQQQgghDCBJlhBCCCGEASTJEkIIIYQwgCRZQgghhBAGkCRLCCGEEMIAkmQJIYQQQhhAkiwhhBBCCANIkiWEEEIIYQBJsoQQQgghDCBJlhBCCCGEASTJEkIIIYQwgCRZQgghhBAGkCRLCCGEEMIAtkTepJR6BAiO/NMCfFFrvTdpUQkhhBBCpLmEkiwgH3gXkQTrT8DhpEUkhBBCCDEHJJpkebTWJwCUUh6ttT+JMQkhhBBCpL24kiyl1IcAPzBPKfVOQE3426G1/n3ywxRCCCGESC/xjmRVEUmysoB5RBKr0b+tQF60Nyql3gsUa61/mlioQgghhBDpI64kS2v9bQCl1DVa6x+N/H3t6N/RKKXWAlcAuxOMUwghhBAirST6CAcd5e/JV9Z6F/DHBMsSQgghhEg7iSZZC5VSO5VSrwBVSqnamQShlLpZKVWnlKrr6uqayaaEEEIIIWaFeC98P5/I87HeM+El38jr/6a1/r94g9Ba3w7cDlBbWzvtyJgQQgghxGwX74XvvwXuGfl7FfAq4A7gfKXUPcCngbiTLCGEEEKIuSbeJKudyMNH/wx8CjgGZAOfA+yASymltNanjUZprTcCG2cSrBBCCCFEuoj3mqyNwJuAe4HLgD6gBLgGaAP0ZAmWEEIIIUSmiTfJug6oA44TuQ7rI8AzwPyRvz1JjU4IIYQQIk3FnGQppazAQWAbcAOwg8j8hZ8BfgzcDbgMiFEIIYQQIu3EfE2W1joEfEgp9YzW+iql1O+JPOH9JaCQyCjXfYZEKYQQQgiRZuJ9hMOfgBVKqTuIXIe1ArgEeBxwAMuTHqEQQgghRBqK95qsrwJHga8AjUAN8GXgd8A3AJkcWgghhBCC+OcubFBKPae1blFK/YRIwnWN1joAoCKytNY+A2IVQgghhEgb8T4nC631V0b+e88kr/0mGUEJIYQQQqS7ROcuFEIIIYQQU5AkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQwgSZYQQgghhAEkyRJCCCGEMIAkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQxgSpKllKpUSj2mlLpbKVVkRplCCCGEEKlk1kjWF4BbgFuB90y1Yn3nMGd+5VF+8cyRU5Z/8PdbueLWZ9lyrPuU5ffUNfHrTUcZ8gYSDs4XDPFCfTe7mvoT3gZA15CPjYc6aep1x7R+Y7eLjYc66XP5Z1RuOjvSMcSqLz/CDx8/mOpQDPeGnz3Hqi8/wrce3EvnkJd3/fZlthztSXo5Xn+QO55v4C9bjhMOh8eWH2kf4qr/3chbf/XiKes/f6SLK259lo/cuS2m7c9kP2/t9/D/frqZp/d3xPTe/7xnFx/4/Vba+j3Trhut3kPeAL/edJR76ppOWX97Yy+vv+05Pn/XzphimUm997UOUvPFh7n5zq0xvddInYNefrmxnkf3thmy/eePdHHb00do6BrmWJeLtV9/nDueOxbXNkKhEN9/7CBfvnc3fcPxHR+Pd7u44efP8+EY9+fxdhzv4zP/2Mk925umX3mC8e3d1Ofmsluf4YFXWuLeTqKe2tfG8i89zFlffdS0MuHU9vYFw2w63MWAO/Hv4/FcviB3bTvBlT94hpVffoQbfv4cj+9t49ebjvLS0W601gA8uqeNXz5bT+egN6Fy2ge8bDzUSdvA9MeZUbF+f5uVZFUBe4DdwJKpVvQEQrgDYX745OGxZb/ddJSNh7s40efm4399ZWz5i/Xd3L29mWcOdnLni40JB/fysV62NvTyzMFOTvTEdgCdzCN72njlRD/372whFNZTrusNhHhgVyuvnOjnsX3tCZeZ7rzBML6Q5ufPHk11KIZ6Zn87u1sG8YU0v3vhON1DfnY39fPFe/ckvax7tjfz+L52HtjZyiN7Tu5bH/rjNhp7XGw/3sdX7jtZ7qf/sZMTfW6ePtjJbzdN3w4z2c97XX7qO4f5z3/tnrac32w+yiN72qhr7OXz9+yadv1o9b7zxUaeOdjJ3dubeXlcUvuZu3ZyqGOIB3a18o8tJ6bd/kzqHR75MnjiQNe05RjtN88dY9OhLv7wQiMH2waTuu0Bt59fbjzKC/Xd3PZMPS5/kAFvkO88eiCu7fx1axMP7Gxh46Eu/vfJ+H6A/dtftnOgbZDNh7v43zh/vH3zwf1sbejhp08doWfYF/P7JrZ3vztAc5+HL92X/P4dzUf/vINgGFz+MK/50UZTypzY3gPuADuO9/Hkgdh+RE1n0+EufvdCIw09Hvwhza7mQb7z6EGeOdjJn18+wdEuF4fbh/jDi41sOtzF7XEm86Me2NUy0rdbY1o/nu9vs5KsfGAj8DQwb+KLSqmblVJ1Sqm6kHvgtMAqCpwopQDIsp98pSDbzshi8p32hINz2iwjcZy6/Xhl262RGG1W1DTrWi0Kx0i5zhmUOVdM93mlu4o829jfauz/INuAts8b1xcKc07+neOI7J9KKSoKssaWj+7zSimqip3Tbn9G+/nIG7Js09e7PD9rrN8XxtC/o9U7Nyvy2SsFec6T7ZA9/vMoOvl5RDNX+vfo52GzKHLHfR7JYLdZxuo9ur8BWFV8Pbw492T7FeU44npv3lh7K8rzp2/X8XKyIjHbrRZs1thjntjeo++0W81re6vlZLxl+fF9Zoma2N6jzZysfT7bbsVuObUdskY+U5tV4bRbyHFYx+qe60hsf3aO9O3RPj6dePq3Gh1uM5JS6s/A/wGXABdrrW+Itm5WXpG2FlZQnpd1SgfpGfbjD4UpyXWccoBu6fMQDIeZV5gd04F7ttp54AjWggoKs+1Ul+SkOhxDeQIhfMEwuQ4rLU0nqKmpSXVIpmtsbDSs3qGwpmvYhwIqxiUqgZCme9iHzXLql0+fy0/7oBeb1cKKirykxzO+vRsaGskrq6Ik1zF2YJtKPP07Wr3DWjPkDWK1qLEvYAC3P0TnkJcsm4WqwuwZ1XE6u0b6d0mug/lFxpY1nVBYM+wLRpKsrOm/lPzBMO5ACKfNElObjW/vI0cbsBdVUpGfRWF27D+EtYauYR/hsKYsPwubJfaEJ1p7x2LQE6DH5Sc3y0ZFnAna+HrXH2sgu3geZflZcceQqEFPgKY+D1alWDUvb2z/N5rRx/NAKExrvxe3P4jDZqEo24E/FCbHYR3bp9z+EIFQmNwsW1z7SmT7OrJtq2Xsh1e8tm/frrXWkx6gzGl9+DjwC+A84NNTrplfzrz3/phsu5W6b14LwN6WAb710H4AVlcV8PU3nAXA37Ye5+fP1AOwYXExt910rkHhGy+ragVV7/sJAHXfuy61wRjIGwjxq01H0RrK8rP46SfeTF1dXarDMl1tba1h9b7j+QYeHxnCfsd51dx47gIAfvzkIV4+1gvAv1+xjMtXVQCw/ptPoD2Rayjed80KPnnVyqTFMrG9P/X2a7n0C79lYXE2P3zbuinfG2//jlbvZw52sKspMkJ+w7r5LC2PJJLvu2Mr9Z1DAHznxnPGPg8jZFWtYN77foJVQd13U9u/H9zVSn3nMADvvKCayoKpRy9/93wDg54AFqX4+JXLsE0xOjOxvW9+0zVUf+AnFOc62HzLVTHH+OjeNv7wQiMAl68q59+vWB7ze6O1dyyuv+05+tx+lFLc87GLmBdj8j2x3v/2ltew6mM/p7LAyb/+/VUxlz8T537rCSpdkX68YXUFt7/vPMPLNON4/vvn6vnOo4fIDYNVwfzibOYXZlOa6+BTr15JWZ6DP750HIDFpTm86dyFcW3/r1tO0DFyLdfNly2N6YfHREqpHdFeM2XoR2s9AHwTeElrfdpVeeNPF4ZHThcWjBvGLs/PGjulsWDc6YwV5Xljw4Q1pbkG1sA8aTwYFxO71ULByGmdslxzhrQzTXVJ5IvBohTVpSdHRRcVR/62WRQLxo2mlI60g0Up1iwoTGosE9t79Ps5lpGjePt3tHqX5kZGJGwWdcpoysKiyLHEbrOwyKTR49lw6nC0vbPslpi+UMryIusX59pPOSU1mYntPbp6Rf70p6HHW1ScM1ZWdXF8bROtvWMxmnDmOWwUZsd+fIpWb6NHSMcbjV0p2FBdZEqZZhzPK4tycdgip2CtFkXRSJvmOm0U59jJzbKNnZouzYtv9DHynkjc+U7b2CnApNJaG/4/IAe4DyiZbt0zzl6rv/3gXj007NPjtfa59Y7jvXqifc39+pkD7actTzdLV5+jb/r1i7qr35XqUAzn8Qd1U69LB0NhvWHDhlSHkxJG1/tA64Cu7xw6bfmupj59ovv0fezWR/brjQeN6Ufj23vt+nP1toYeHQiEYnpvvP07Wr3b+j263+U/bfkTe9v00UnWT7YVZ67Rn/hzne4b9BheVixa+tx6yBuIad1AMKRP9Li0xx+Maf3x7X3WmnX6Ty82aI8ntrLGa+we1vta+uN+n9bR23s6bl9QP7K7Vbf1u+N+7/h6n7N2vX5kd6v2+WL7zJLlWw/s1X97udHUMo0+ngdDYV3X0KN/8sQh/dS+Vt077NW7mvp0n+tkjjDsDejmPrcOh8Nxbz8UCuumXpd2z6CtgDodJacx65qsbwFXA/uBR7TW/4q2bm1trZbTR5lD6p1ZpN6ZReqdWTK13kqp7Vrr2sleM+WaLK31V4CvmFGWEEIIIcRskPoLBIQQQggh5iBJsoQQQgghDCBJlhBCCCGEASTJEkIIIYQwgCRZQgghhBAGkCRLCCGEEMIAkmQJIYQQQhhAkiwhhBBCCANIkiWEEEIIYQBJsoQQQgghDCBJlhBCCCGEASTJEkIIIYQwgCRZQgghhBAGkCRLCCGEEMIAkmQJIYQQQhhAkiwhhBBCCANIkiWEEEIIYYAZJVlKKTXu76VKqZoZRySEEEIIMQfY4llZKfUrwA8owA60KqWGtdY/An4MPAD8LulRCiGEEEKkmXhHss4C9gEbgTXAt4ArlFLPAdu01pJgCSGEEEIQf5LlBbqAdsAH3A0cAj4BBJVS1uSGJ4QQQgiRnuI6XQhUAVcAg8A84Pcjy9cClwHZwNeSFZwQQgghRLqKN8n6CRAANPBt4AJgCNgPPAQsiPZGpdR7gWKt9U8TilQIIYQQIo3ElWRprX+rlLoEOI/IqcYq4Pda61emep9Sai2REbDdCcYphBBCCJFW4r27cCuRUawVgAvoBb4/8iQHO5Cvta6d+D6t9S6l1B+BdTMNWAghhBAiHcQ7knX+yEjWlUTuMnwzUAx8XWu9NdEglFI3AzcDVFdXJ7oZIYQQQohZI667C5VS5xO5q/AxoBH4IfBVItdmoZT6t0SC0FrfrrWu1VrXlpeXJ7IJIYQQQohZJd4L338L3DPy9yrgVcAdwAVKqSHg08D/JS06IYQQQog0FW+S1Q78Cfgz8CngGJHHNnyWyDVZLqWU0lrriW/UWm8k8hBTIYQQQog5L96HkW4E3gTcS+S5WH1ACXAN0AboyRIsIYQQQohME2+SdR1QBxwncm3WR4BngPkjf3uSGp0QQgghRJqKOckamTLnILANuAHYAbwL+AyRyaHvJvJYByGEEEKIjBfzNVla6xDwIaXUM1rrq5RSvwfygJeAQiKjXPcZEqUQQgghRJqJ92GkfwJWKKXuIHId1grgEuBxwAEsT3qEQgghhBBpKN5rsr4KHAW+QuQ5WTXAl4HfAd/g5ITRQgghhBAZLd4nvjcopZ7TWrcopX5CJOG6RmsdAFARWVprnwGxCiGEEEKkjXifk4XW+isj/71nktd+k4yghBBCCCHSXbynC4UQQgghRAwkyRJCCCGEMIAkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQwgSZYQQgghhAEkyRJCCCGEMIAkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDmJJkKaUqlVKPKaXuVkoVTbVuKKzZcbzvtOVNvcO8WN9lVIgMeAJ4/KEZbUNrTc+wj2AoHNP6gVCYnmEfWmvcviBfv3/PjMoX6SMY0mw82Gl6uVsbezjaOTyjbcxkP4/Wv6Px+EMMeAIxrz/kDeDyBU9bHq1/H+9xMeSNbfszqbc3EOK2pw7F9D4z9Lv9+IIzO95FM77eM9nP3f4ggzG2zUSJ7uejbRyIsY2jiXc/T5ZnD3Wwr2XA1DLd/iD1HUOEw2G0hl6XP6nb73P5ONIxxON72/B6T+/bAN5AiH534uWGw5F2D4V1zO8Zv59PxZZwVPH5AnALkAW8B/hZtBWPdbv45N92cNGyMv73rWsBONo5zNt+/RL+YIgrz6jktpvWJzW4A22DPL6vHYfNwrvOX0xhjj2h7Ty+r50DbUNUFTp5x/nVU64bDmv+sa2JriEfaxYWcrTbxR9eOsHftjVz6NuvS6h8kT7qO4f45N92sLa6mD9/6AJTyvz2g/u4a3szFovi1+/awAXLShPazkz288n6dzQD7gB/2XocfzDMa8+ax+qqginXb+x2cf/OVqwWeGvtIioLnED0/v2XLcd5YGcreVk2fvCWtZTkOQyr95HOYX70VD2/e+E4u772minfa7Ttx3vZfLibvCwb775wMdkOa9K2PbHeie7n3cM+/rGtiUAozPVrqlhekR/ze2eynz9zsJPdzQOU52fxzvOrsVhUzO8dL579PFk+8/dXeGRvO0rBL246l6vPrDS8zGAwzBf/uYeOQS8XLCmh1+Xnzhcb2bC4mMtWls94+9uP9/KHFxp58kAHOqxZUp7HY5++7JR1hn1B/vLycdz+EFedUcHaRUVxl/PArlYaul3UlOVw4/qF064/cT+fimEjWUqp9yqlPqWUygbeDnwDGASWTLLuzUqpOqVUnXcokv3Xj/sVsuNEL/6RX12HOoaSHmtLnwetwRcI0zXsS3g7zX0eANoHvdP+2vUFw3QNRcpq6fecslzMfaGRHz/Hu12mlbmzeSAymhQK89KxnoS3M5P9fPSXYn0Mowxdw158gciv45Y+z7Trtw54CGtNIKTpGPSOLY/Wvw+1R44lw74gTX3Tt0My+vdwgiMzyTRaj2FfkH5PckcdTmvvBPfzzkEf/uBI2/d7p3/DODPZz0c/m64h34yOxfHs58mypyVS73BYs+mwcWd9xhv0Bsf62tGuYYLhyGc2fp+fieY+D33DXgIhTVhzSr8e1efy4x4ZpU603NH3xXKcgej9ezKGJFlKqbXAFYACPgQcBD4G/BuQN3F9rfXtWutarXVtaWkZVYXZfOhVNWOv37huPucsLKIsL4tPXLks6fHW1hRTXZLDWfMLWFKWm/B2rlhVzvwiJ5evLMdmnfqjzXZYuXRFGQuKsrl0RTmjP5jOW1yUcPkifeRlWSnNy+KD4/Zzo332mhVU5DtZUZnPhy9dmvB2ZrKfFzhtp/XvaJaU5XHW/AKqS3KorSmedv21C4tYWp7Lysp8Vs07OfIRrX+/47xFLC7N4VXLyzhnwdS/RmFm9bZZFFYFr15dMW05RrtoaSkLi7NZX13EvJHRvmSZWO9E9/MVlXmsrsqnpiyH9dVFcb13Jvv5ZSvLR2Ivm9EIXzz7ebLc8tpVFGXbWVicw+des8qUMkvyHLx+TRXVJTm84/xq8rJsLCjK5pLlZUnZ/kVLS7l4RQXLy3MpyXPw1g2njzItKMpm7aJCFpXkcP6SkoTKuXJVBfOLnFx5Rmz9c+J+PhU13fnERCmlrgDWAQuJjGA9AnwJeEVr/a1o78srKtHFFfPJtlspyJ76tF0wrOl1+dFak++0k5PEYW+zHaw/Rqz1Tmcaxs59O+1W2lqaMrLefR0t1NTUpDos0zU2NhpWb28gzMDIyExxjgOHLZIIuf0hhrwBlFKU5DqwJXgKaCaMrLfRItdvhbFaFGV5WVOum+79e8ATwBsIxVTXqWTq8dyI41qy2iSW7ZfmZZHI0WH79u1aaz3pLy8zrsnyAX8DvgacR2Q06xRKqZuBmwGKK+bz2V/8iwVF2bztvEVTbvhY1zD372wFYN2iopiz0Nlo0cqzY653OvMGQvx60zHCWlOen8V/vfe6jKz3Tz7xZurq6lIdlulqa2sNq/eWYz28eDRyeuiaMys5e2R06tmDnexs6gfgjesXzGi0OlFG1ttodzzfwIAngNWi+Pcrlk05ipfu/ftPLx+ne8iHRSk+evlSnPbEfrhn6vHciONastokmr9tPUH7gBel4COXLiU3K/60SCm1I9prZiRZdcBa4D+Bb2itOyauoLW+Hbgd4Ky16/Xqqnw2LJ5+2G9JWS4XLStlyBvkgqWJDRPOFgXZdmKtdzpz2q289uxKGrpcnLu4mO9maL1/kuqA5qB11UW4/SFsVnXKRfIXLC0hGNbkO23UlOakMML09Nqz57G7qZ8VlXnTniZN9/79mjMr2XG8jyXluTP6Ms/U4/lPDChjtE2WluclPcECuHp1BTuO91FdkptQgjUdM5Ksh4CfA+8HPj/dytl2K9eeXRXThpVSXLg0sTukZpt46p3uzphXwBnzIl+CmVpvkXxZNuuko9k5DhvXmHCn1Vy1oCibBUXZMa+fzv27ssDJ686ZebzpVu+ZMPq4lqw2iaYi32loWxmWZGmtNwIbR/75UaPKEUIIIYSYjeSJ70IIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQww65KsYV+Qf2w7QVOvO9WhmErqnVn1FqnXMejlrromnj3UiVHTi42S/dw8fS4/92xv5rG97WMTNZtN2js5zOyjRpl1SZbLF6S138vz9d2pDsVUUu/MqrdIvZeP9dDS52HniX7aB72GliX7uXm2H++jqdfNgbZBGrpdKYlB2js5zOyjRpl1SZbNEglpYXHsTxieC6TemVVvkXoLiyNT7OQ7bRRlOwwtS/Zz8ywozkapyJQv5QZMKBwLae/kGP38zOijRjFjWp24lOQ5eP/FNRTnpucHmiipd2bVW6TehsXFLC/Pw+mwkGVL/pxo48l+bp7VVQUsKM7GYbUYMtddLKS9k2PD4hKWl+eb0keNMuuSLAUZuWNKvYUwX2GO3ZRyZD83V4HTnHaNRto7eczqo0aZdacLhRBCCCHmgrhGspRSC4F3Ad3ACeAZrXXIiMCEEEIIIdLZtEmWUuoYcACwAncAOUAPcA1wGfAVIwMUQgghhEhHsZwuPArcCPwVOB/YDbwT+EGM7xdCCCGEyDhRkySlVI5S6huABn5EZDTr7pGXi7TWXVrrL5sQoxBCCCFE2plqJCoAPA0sBhqInBq8aeQ1ZXBcQgghhBBpLWqSpbUOaK03A58Ato38735gO2DYXAGeQIjfbD6K1xuMaf2GrmG2H+8zKhzTDHkDfPlfuxl2+VMdijDIlmPdfPlfu2kfGE51KCkTCGlerO8mGAzHtH6fy09Lvyfm7bcPeOka8sW0bjgcZsuxHpr6jJ/6xOULcuujB3C7A4aXNR2tNU29bgY8xscSb3uP1zPso20g9rYfr6XfQ18Cx1KvP8iL9d10D8/s6eJhrTne4yJs8rQ+v3y2nod3t5paphmaeof5zeajdA54GXAHaOp1nzLNzpA3wIket+mfdyxiubvwD8BzQCewBfhP4C1GBdTY7eK2p4/wyJ527v34q6Zc93DHEF9/YB+hsOZN5y7g7edVGxWW4Y73uPnbtiaePNDB1i9fk+pwRJK1Dwzznt9tJRjWPLK3nfR8rN7MtfS7+enTRzi3upj/fN0ZU67bPezjr1tOEAprrlhVzvrq4inXP9wxxMO721AK3nzuQhaV5Ey5/u2bG3j2UCcOm4Vb37yGqiLjns7d2OPmt8838Ojedp79wpWGlROLF+p72NbYi8Nm4b0XLSbfwGdKxdPe47UPePnHtibCWnPNmZWcvaAw5vfuONHHpkNdWC2Kd15QTVkcT32/9fFD7GsdpMBp4xfv2oDDlthlxz0uP//a0cKZ8wt47VnzEtpGvN53x1ZeOtqNUgpPIMRbNiwypVwzvOP2LQx6AvzxpeO84/xq/MEwFywt4eJlZXj8If788gm8gRDrFhVx5RkVqQ73FLHsQfu11u8AngU+B3gw8CGmo8lpj2v6X6Nt/Z6xCUDbBtJzXqNRo/m3yydPxJiLOgf9hEYa2ePP3DYOjwxodA5N318HPYGx/t0fwwjQ6MiF1sQ0SjM6F5o/GKbH4BHk0V/dA97Uj2T1uSN19QfDuA3eF+Np7/EGPAHCOva2H69/pH6hsGYwztG6ruHI986QL4jbH9vZlMmEx/Zb885MjI76aa3Z3zJoWrlGCwaDuHyRthjyBvCPjIr2uSJt6wmE8AYi+3GfiZ93rKZMlpRSVsCmlMoBrgcuAM4C7lVKvVdr3ZTsgApz7NSU5fLvly+fdt1LV5RxuHOIPleAd56fvqNYAHlZNkpyHXzs0qWpDkUYYM2iEi5fUcau5gHec2E1f34s1RGlRkmugzULC3nzuQunXXdJWS4XLC1h2Bvk/CUl066/rroIlz+I1WJhdVXBtOt/6FVL+POWRqpLc+MaKUlEQbadeYVOPh7Dcc1ol60ox25VlOdnUVngNLSseNp7vBUVeZxXU4I3EKK2ZuoRzIkuWFJKMKTJc9pYUpYb13s/culSHtjVyrmLiinKSfyJ7QXZds6Yl09tzfT7bbJ898Zz+MI9uynMtvFfr1tpWrlGs9lsfPiSpTy2r50b1y9g7aIiuof9XLSsFIjsY1eeUUFbv4cLlpamONrTqfHnNSddQalFE5MppdRZgFtr3ZDsgGpra3VdXV2yNzvr1dbWIvXOHFLvzCL1zixS78yilNquta6d7LVpTxdqrZuUUhalVLZSyjaybF88CZZS6kKl1KNKqV8ppc6PPXQhhBBCiPQU61V9FxG5u/DyBMtZB9yitf6Y1nrrVCsO+4L8ZvNR2ibcUfTDJw7xkTu30dg9N+/Maun38OofbuTp/e2pDsVU3cM+PvSHrew6kf53iE5na0MPv950lKYe4+9mm62i9e9o9rUOsOVYz9h1GFMJhTXbj/eyq6n/lDuP/MHIXYT7WgdOWd/jD/HS0R7qO4fiq0QCZtN+bnS9m3rdvFDfzYA7MKN672keYFtjL8FQfHcmRmtvM8W7nydD54CXj/2pjq89sMe0Ms0w4A7w/UcO8sZfPM9tTx9maBZc1xiPqNdkKaUswPuBILAC+BbwRqXUdeNW26q1/nsM5awHzldKlQIf11o3R1uxa8jHUwc6Odbl4rtvXgPA0/vbufPFRrTWfOrvO7n/E5fEUGR66XX5Odbt4tP/2Mmeb1yb6nBM0znoY2tDL5+9axdPf/6KVIdjmCFvgJ8+dYRgWHOo3fgv9dlqsv4dTVOvmyf2dQDgD4W5dEX5lOvvbOpn8+FuALLsFs6YF7ku6+VjPWOPeSlw2sfuOtx0uJMDbUMoBe+50EFpHHehxWs27edG1tsXDHHfKy0Ew5HHRCRa76Ndwzx1INL2obDmwjiutYnW3maKZz9Pls/es2ssma0pyeUDl8yN63sf3dvGHS8eIxiKHDsXFOXw5g3xXeOXSlONZNmBt09Ydj6RZ2RdAPwD+FWM5XxBa/1B4LtEErdTKKVuVkrVKaXqfEP9ADjsJ29yz3ac/DsrwVtqZ7vRp7vaLHOzftOxz9F2HWW1KKzWSCvP1X04HuP7dzQ2q0KNdIxY+oXdqsb9bTntb6Ui25y43KIUVos5z1eeDfu5kfW2qJP7+SltEGe97Zbx7RdfjNHaOxVi2c+TJds2Wm9FntOwBwCYzma1YBk5EFgtalb0oXhM1xL7AR8weuuNG2gDXEROH8Y65HIT8GugBDht7FdrfTtwO8DZa9frt9Uu5NWrK8dev3h5OV/+f6vZ0zLA51+zIsYi00tlgZPzaor52uvPTHUoplpQnM31a6r49yuXpToUQ+U4bHz1ujPZ1dzPlasqefK7qY4oNaoKnUzs39HXzebN5y5kyBvkjHn5065/zoJCsmxWbFbFsvK8seUXLCmhMNtOvtNGVeHJZ2FdtrKcygInpXmOGd1JFovZtJ8bWW+71cLbaxfR0u9hRUV+wvWuLs3hxvUL8ARCMbX9eNHa20zx7OfJ8rO3r+f7TxykuiSHt9am99324113ThWBUIjNh7u4+oxKXnOmeZ9pMsSS7n6IyJPe4eTjnNBah4GXYyzHq5S6HwgBH51qRafdypsneYja28+vPm1YbS4pz8/iHx+9ONVhmK4w285337w21WGYYnllPssr4/vCmGui9e9o4jnVo5Ri1SRfyBaL4sz5pz/SwW61GP7ohlGzaT83ut6leVljpyBnUu+aOB+/MCpae5sp3v08KWU6bXztDWebWqYZsh1Wbjp/MTedvzjVoSRkuiTLSuSU4j5gNH2M+7n1Wus7gTvjfZ8QQgghRLqa6uRmGFgNeIlc9K6JjERdB/iUUg8rpZ4wPsTIHGO3PX2EW+7Zxf4U3jFipO5hH1+4exdbG3pSHYowyJ9fauRzd+3k+SNdqQ4lLfiDYW597CD/9c/dHO9xpTqcGZH+HZ9AKMxje9u495VmBtPkbrK6xl7+se0Ejd2p2VeP97j4r3/u5tbHDsZ0N266eeloD3dta+JA2yD3vtLMY3vbCMR552kqRB3J0loHgGsAlFKvAt4JfERr3WhOaCftaRnghfrIXUN/39bEN28wZ4jfTIOeICd63fxtaxPnL5l9T60VM9M77OfB3W0A/G3riRRHkx6er+8eu0vs7rpmPv/aVSmOKHHSv+NzpGOYA22Ru3BfOdHP5SunvrM01Tz+EM8diXxHbU7Rj6i765o51u2CbhfP13dz1Sybw28mBjwBXj4W+YFyoH2QgpH5NheX5sY0u0MqxXqZfj3QOrr+yMNJX6uUMu6e53EWFeeSlxXJBye75mIuGL0LZnlF3jRrinRU4LRRURDpLuMvyhbRrSjPI8tuQSk4c5YfSKcj/Ts+FQVZOGyRtl9QZOzUP8mQZbNQlh/p3wsMnGh8KmfMy0epyONLVsyxY0yOw0pJbuQmjZUVkXo6bBYq8k1JQWZkurkLz9Fa79FadyilXqu1/p+Rl6qAtwL/BrzR4BgpyXPwk3eso9flZ3FpYhdDznYLi7P5zpvOkS/gOcpms/D9N6+hbcDLktIc/vqlVEc0+y0qzeEnb1uPKxBkUbH5zzpKJunf8SnLy+IDr6ohGNZjoxazmcWieMd5ixjyBinOSU2816+dz9rqIvKzbIbfLWs2u9XCOy+oZsgbpCTXwaA3gN1iOeXxTrPVdBe+/wy4YuTvsRPNWusW4MNKqc0GxXWafKed/DTobImyKCUH4Dkux2GTNo5TSZ6DEtL/C0P6d/xyHOn1rCe71TI22pIq6f5jZCrjP990SLxHTbcXj7/iUCmlxj98o5xJnnklhBBCCCGmT7LGP67BDnx+5G9FJMH6hhFBCSGEEEKku+mSrCyl1PNEHt1wFtANPAn8XWs9N2dqFkIIIYRIgunuLvRprS/RWl9OZBqdTwMO4AWl1I1GByeEEEIIka6mG8kaf++s1lq3Ab9USt0HPKiU0lrr+4wKTgghhBAiXU03kvW1cX+PJVxa61bgeuBWpZTcMiOEEEIIMcGUSZbW+tlx/3zjhNfagKvl2iwhhBBCiNPF+sR3tNaDkyxrSm44QgghhBBzQ8xJlhBCCCGEiJ0kWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQwgSZYQQgghhAEkyRJCCCGEMIApSZZSqlIp9ZhS6m6lVJEZZQohhBBCpJJZI1lfAG4BbgXeM9WKLl+Qu+qaaOp1mxIYwIA7wL2vNPPk/g5CYZ3wduo7h7mrroldTf0xrb/9eC931TXR2O3ieI+bK37wLHe+eCzh8tNRptY7U6Wif0fTOeTlnu3NbDrchdbT93vp3/FLRXs39bj58r17+MHjh/AHw3G9t7HbxV11TWw/3jujGNL5eyxef9lynC/cs4vnj3SlpN5aa5492Mk/tzfTM+xLaBsH2ga5q66J/a2nzR4Y1fj+PRWzkqwqYA+wG1gy1YrDviAtfR6er+82JTCAbY29NHa72dsyQMM0H9hUNh7qpKXPw7OHOgmGpu7cHn+IzYe7aenzsPlIF8O+ID3DPn77XGPC5aejTK13pkpF/47mpaM9NPW62XG8j/ZB77TrS/+OXyra+x91TdR3DlPX2Bt3uZuPdEXa7HA3Hn8o4RjS+XssHr3Dfh7Y2cqJHjd/23oiJfVu7vOws6mfE71utjQklhw/czDSt5852BHT+hP791QMS7KUUu9VSn1KKZUNXAL8C1gK5E2y7s1KqTqlVJ1nsA+A+UXZRoV2mvlF2SgFWXYLZXmOhLezYCTmygInNuvUH22WzUJZflak/MJsLCqyvLo0J+Hy01Gm1jtT2UYa3Mz+Hc1of83LslGUPX2/l/4dv1S09xnz8seO58vKc+N67/zCSJxl+Vlk2RL/ekxFvZP1PRaPAqeNioLIfr6kLC8l9S7OdZDtsM6o3PlFzrjeP7F/T0XFMkweL6XUWuBTREaugsA64C7g/cABrfW3or23rKxM19TUJD2m2WjIG8QXDJGbZaP5xAmKK+eTl2XHac+c+xGOHG2QemdQvRsbG8mU/j2etHd68ARCuHxBsmxW8p22hLeTbvWeifHfYx0tTRlZ7/27d2qt9aQd25AkC0ApdQWR5GohcDvwVeBVwIVa66hjcrW1tbqurs6QmGYTtz/IrzdFrs8oyXXw3++/ns/+4l9UFjh55wXVKY7OPItWni31zqB619bWkgn9eyJp7/Twhxca6HMHAPjo5UvJcSSWaKVbvRM18XvsZ//xloys9/tftWS71rp2snXN+EnlAwa11u8GXpwswRp/urCra+rzm3NFtt1KdUnk1MGKyjwcI6cfVlaedjZ1TpN6Z1a9M5W0d3pYOS8fgOqSHLLt1hRHM/tN/B7LFPHUO/Hx0NjVAZcopV4CPJOtoLW+nchoF7W1tebdFpFCSinevGEh/mAYh81Cca6Dj1+5HMcMrgNIR1LvzKp3ppL2Tg8XLyujdnGJtFOMJn6PZYp46m3Gp/IQcA3wa+B/TSgvrYxvoEzaSceTeotMIO2dHqSd4pepn1ks9TZsJEtrvRHYOPLPjxpVjhBCCCHEbJSZ6acQQgghhMEkyRJCCCGEMIAkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJKsJPL4Q+xtGWDAE4j7vcGwZl/rQNyzxqc7qXdm1TtTSXubR2vNofYhWvonfSyjSLI+l5+9LQN4A4lPqJ1qw74ge1sGGPYFk75tSbKS6IFdLTy5v4O7tjURDsf3TNVel58n9nXw+L52g6KbnaTemVXvTCXtbZ4tDb08sqeNu+ua6Bj0pjqcOS0QCvOPuiae3N/BI3vaUh1Owv61o5kn93dwT11T0rctSVYSeQORX6n+UJi4H1s/8gZfpv3SlXqnNg5hDmlv04x+xlqDLyCft5HCWhMY+bzTed8eHYUzog5mTKuTMa5bU8W+1kGWluVitai43luYY2fD4mLWLioyJrhZSupdlOpQhAmkvc1z4dIS7BZFvtNOdWlOqsOZ07JsVt6wbj7He9ycs6CQH6Y6oATdsG4BB9uHWFWZn/RtS5KVRGV5WVy+sjyh92bZLFyW4HvTmdRbZAJpb/Nk2axcvLws1WFkjMWluSwuzU11GDNSWeCkssBpyLbldKEQQgghhAEkyRJCCCGEMIAkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGGAaZMspdRCpVShUip/5N/vUhH/Tyl1XbID6hj08tE/1vHU/umnnxj2BvnSv3bz8b9sZ3/rQMJldg56+eNLjfxrR3PK5hY73DHE2V97jC/9a3dKyk+VeNo73X3mHzt57Y838auN9Qx6A9zxfANHOoZMKz/afr7lWA8f+1Md33pof0z7/+7mfn73fAMv1HcbGe6ckqn9OxX7+f7WAT7+l+186V+7GfbGNxfdQ7taufmPddz29JEZxXCi1821P97MH19smNF24jGTes9WTb1ufv9CAw/tauWJfe389rlj3L75KH94oYGmXjcA/kCIbz24n4/9aTsvH+tJccSnmzLJUkrdAFwBLAX+ObL4fcBHgMuBxmQH5PKF6PcEeGj39F+6Wxp6ONrlonvYz8N7Ev+S3tU8QM+wn+M9bk70uhLezkz4g2H8wTCPzKAe6Sie9k5nTb0utjb0MOwL8sCuVjz+EAOeANsa+0yLIdp+/tDuNvrcAfa2DLAvhh8rW471MugJsLWhl0AofafSMFOm9u9U7OcP72mne9jP0S4XWxri+9J9eE8bA54AL9R30z2c+LyHbn+IIV+Af+1oTXgb8ZpJvWer7cf76HcH2NMywMvHemjp9/DysV763AFeaeoHYF/bIHtbB+hz+3lot3mfd6ymG8mqAj4HVAJKKVVN5CnxW4DzgbOSHZDdFpmO5tzFRdOue/aCQvKdNqwWxfk1xQmXubQ8Mg1OvtPGvMLshLczE5aRaXhWVyX/sf6zWTztnc7mFzpZUByZ4mP9oiLs1kjXW1GZZ1oM0fbz2ppilIKyPAfLK6aPZzTmpeW5Y/UQU8vY/p2C/fy8muKx/fzsBYVxvffc6iIAaspyKclxJByDfaS9N9QUJbyNeM2k3rPV8oo8lIKqIidLy/PIcVhZXJKDRSmWl0f2qWXleZTlZaEU1NaUpDji0ymto09lrJTKBj4OXAqsA54hMoJ1LnA9ENZa/zWZAdXW1upnn3+JfKc9pvWDwTD+cJgcx8xmCAqEwliVGjsYmm3Dhg386/HNLC5L7+kJ4lVbW0s87Z3OQqEQ/Z4gpXlZ1NbW8sLLW8iyWU2NIdp+PuwNkuOwYLHEljT5gqGEYq+traWuri7u96W7TO7fqdjP3f4gDosFmy3+HwFD3gC5DmvMfWEytbW1PL7xBUrzshLeRiJmUu9kMKJ/+4Nh7NbI8cofCmO3WAiGNY5xdQyFwngCYfKcqZkpUCm1XWtdO9lr07XEt4BlwNeAvVrrDwCHgcVAGbA5mYGOiucL12azzDjBgsgvrlQlWABKqYw7AI/KhAQLwGq1nnLQNfuLB6Lv53lOW1xfKqmIPZ1lcv9Oxb6S47AlnGjkO+0zSrBGmZ1gwczqPVs5bBaUUiilyLJZsVjUKQkWgNVqSVmCNZ3pWuMW4F/AAHD3yDI7kSTr9cC7YilEKXWhUupRpdSvlFLnT7Wu1pqWfncsm51TMrbeRC6OzQShsGbAk9q6unxBfMGQqWXOhnoL8/iDYZr6IseyjD2uZWi9jdLU507ZTWkzNV3qNw+4GdgIuJRS7wVOaK0fAB5QSq2OsZx1wC1a6z3TrdjU5+Gz/9jFVWdU8NHLl8W4+fSXqfXudfn53XMNnL+khFctL0t1OIYJhzV31TXRPuBl3ch1H2Y71D7Eo3vbcNqt3HR+NYXZxo8gzoZ6C/MEg2H+85+7ae33cPHy0ow9rmVqvY3w06cP82J9D/OLsvnBm9ek3UjddEnWHwEP8DZgPmAFDiulHiAyCpYLXBlDOeuB85VSpcDHtdbN419USt1MJJkjp6QSgANt5t3yOxsEQ5Fr4zKv3pFfJ8d73LxqeYqDMZAvGKZ9IHK30ome1PzCPdHrRuvIHV9dQ15TkqzZUG9hnkFvkNZ+DxBJ6jP3uJaZ9TbCofbIZ9ja72HQG6QkL/EbElJhupTwjcAB4C9EThf+CsgB/qK1vl5rHUuCBfAFrfUHge8C75/4otb6dq11rda6tqysnHmFTt6yYUGsdZgTInd8ZV69c7NsVBRkcfGy0lSHYqhsh5ULl5ZSUZCVshG7c6uLmF/kZNW8fGpKzbk+aDbUW5inJM/Ba86sHDmWLcrY41qm1tsIbz53IfMKnbzmzMq0S7Bg+pEsH/AA0AQs0Fq/qJT6CXCLUsqutY71QoubgF8DJcCUJ1bL87P46TvWx7jZuSNT652XZeNdFyxOdRimuGhZKRelMJkszcvi7edVm15uqustzPWhS5eO/Z2px7VMrbcRrl5dydWrK1MdRsKmHMkaSaK+obU+TuS6KoAAcFEcCRaAVyl1P/Bh4DeJBCqEEEIIkU5iuYLMM/LfNwDoyIO14rrMX2t9p9b6Bq31m7TWXVOtGwxr9rcOnnYnQeegl4Ptg4TCpz7Xq7Xfw5GOIaZ63lc68AXDPLSrdc5MhzCVfref/a2DeAOhqO09Fx3vcfHQrlYG3P6UlK+15kjH0Ng1M6NCYc3B9kE6hxJ/wvVUxrd3pkrn/u3xh9jfOpjQHaLpVm9vIFLX/hT10ZkIh8M8tb+dV06Y93R9MKd/t/V7eGhX64yewh+NLxhp8z6XMW0ey4MlRr/9xvcSwzKaPpefx/e1c6w7j+vXzAdgwB3g79uaCIU1bdVerlxVAUTmvburrgmt4VXLyzh/yex72musWvs9/Onl4+w40cdXX5/0B+nPGoFQmL9va8LjD1FTljNpe89Fbn+Qrz2wD48/xEspml9ra0MvLx7tQSm46fxqKgucAGw+3MXOpn5sFsV7L65J6gXxE9s7U6Vz/35wVyst/R7ysmx86JIlcT1PMN3q/ejeNhq73WQ7rHzokiVpNaPBnS8d57G97SgF/33dmaY89d2s/v2NB/fR5w7wxP4ObrspuadhH9vbzrEuF067lQ9eUpP057rFkmTNV0q9CKwa+a8CViqlXgZytdbnJDOg0QEpb+DkyIY/FB4bwfL6T2bL3kBobH1Pmv9KHq2Hy5/e9ZhOKKzHRq28gfCk7T0XBUbmroPIvGap4B0pX2vwjfu8R/tOcFzbJMvE9s5U6dy/R/cPXzAU96/rdKu3xx/ZR/3BMOE0Ozvi8kXGQbTGtJFDs/q3e2QfdPuTX6/RY2EgFCZsQBViSbJatdZvUEo9qLV+PcD4v5OtMMfOuYuLWbewaGxZeX4W1549j64hH7Xj5ihcXJrL1asrGPIGT1mejsrzs7h0RRk3rJ3bd6M47VbesHY+jT0u1i4s4juTtPdcVJjj4N+vWMaOE/28fk0Vz/3A/BguXFqCbWRus+rSk786L19ZTt7IXZ7l+cl9SvXE9v5hUreePtK5f/+/c6rY1zrAsvI8rHHOipFu9X7d2fPY1dxPTWlu2s1q8L6La7BbLZTlObjQpBtNzOrfn3n1Sp470s1VZ1QkfduvOauSXc0DLCrOJtuR/DaPmmQppZYCPyPyLKzRZYrISJZhKX6WzcLlK8tPW766qoDVVaevv2aOfDnnO2184qoVqQ7DFDVludSMTDESrb3noktWlHPJitTVNctmnfQxCrlZNi4zsA3Gt3emSuf+XZ6fxRWrEvtyS7d6F+c6Eq5rquU77Sl58KkZ/Xt9dTHrq40ZSCnKcRj6HTTVCedm4B5guVLqFqAeeAp4wrBohBBCCCHmiKhJltbar7X+PZGntV8NDGitrwZeS2T+QiGEEEIIEcW012RprbuUUtcBoyfVNXC/oVEJIYQQQqS5WC58R2sdBI6P/B0mMr2OEEIIIYSIIn0eAiKEEEIIkUYkyRJCCCGEMIAkWUIIIYQQBojpmiwzDXkD/P6FBi5dUcbyinwgMnfWx/+6g/YBD/9+5TKuX5MeD7aLx7GuYc7/n6d4+3mL+NxrVqU6HNNM1t5z1efu2sm+lkHedK6x+29Tj5sfPnkYu03xn9euoiwvMn1O56CXx/e1k+e0cd0583HYIr+xmnrdPHOwM/LQ37PmxTVtiohNpvbvRLUPePiPv+/E6w/x1etXU7vEnIdrzsSmw10c7RzmIpMeBDrRg7ta+M4jB8nLsvGXD11ARaEzJXEY4Q8vNHD/zlYWFGfz/osXc7B9mGy7levWVOG0z+6Hxs66kSy3P0S/O8DWhpOTXD5/pItD7ZEJSv++tTmF0RnHEwjj8Qe5d0dLqkMx1WTtPRc19bp4+VgPQ74A9+00to0f29dG24CHEz1uNh48OR/7ruYBuof9NHa7OdHrGlu+40QfvS4/h9qHaB80ZpLoTJep/TtR/3qlmZY+Nz0uH3/Z2pTqcKbl9gfZcbyPAU+ArQ29KYnhDy8eZ9gboH3Aw5+2HE9JDEYIh8M8vLuNziEvu5v7eWh3O11DPk70ujnW5Zp+Ayk265Ks0Qk5l1fkjS1bu6iQwmw7SikuWpa+k0BPxTYyerBmofGTes4mk7X3XDS/0Mn8omwA1ho8S8G5i0uwWxVOu5V1i06WtbQ8F+vItDrzCrPHli8rz0MpKMl1UJLrMDS2TJWp/TtRV6yowGm3YrUorkiDGSGy7VYWFkf6VKqOZZevLEMpRZbdymvPqkxJDEawWCycvbAQh81CRb6Tc6uLsVkUuVlWFhRlT7+BFFN6lk2CWVtbq59/actpQ4B+f4ghf5DSvOTOrTZbbNiwgfue3MSikrmdbExUW1vLZO09F4VCIXpcfioKsqmtraWurs6wstz+IBbA6Tj1igB/MIzVok6bg84XDGG3WAw/VWh0vWerTO7fibb3sMdPIATFeemR+Gut8QXDOO3WlO3nnQNecrJs5DlTcyWQkfXuc/nIdthw2q1Rj2OpopTarrWuney1WXdNFjDpF67DYaXUgMkbZwulVMYdgEdlQoIFYLVaqSgw55dXjmPyrj16HdZE6TYZbrrJ5P6dqLzs9EiuRimlUn4sm0vXYU1UnHtygCXacWw2Sp9IhRBCCCHSiCRZQgghhBAGkCRLCCGEEMIAkmQJIYQQQhhAkiwhhBBCCAOYkmQppSqVUo8ppe5WShWZUaYQQgghRCqZNZL1BeAW4FbgPVOt6PaHuH3TUQY8/lOW728b4PG97fiDYeOiTKEBT4DP/P0V2gfcqQ7FVNHaey5q6nHzyO42hrwBQmHNgbZBAiHz9udwOMzTBzrY1XTq0/X9wTCP723nYNugabFkmtm0n5vZ3qmod7T93Eyp6N/BYJAfPnGIu+tOmFbmREbVe29LP99+cB+H29PvGGXWc7KqgD2AA7hpqhWb+tzc+VIjLzf0csf7zxtb9j8PHSAY1hzsGORTV680PmKTNfW5eXhPG3XH+3juP69KdTimmay95yK3P8hXH9iL2x/ixaPd9Lr8PLa3nZWV+Vy3psqUGP708gke2dOGUvDf153J2QsiTx//v031vFjfg82i+N5b1rCoOMeUeDLJbNrPzWzvVNQ72n5uplT07//4+y6eO9KFUgq71cIb1y80pdzxjKr3B/9Qh8cf5LF97Tz/xauTtl0zmDWS5dWRR8tbgdOeyKeUulkpVaeUqgsM9wORiYNHubxBgmE9sjxoRrzmG3nwvscfSm0cJhudcGB8e89FgWAY38go7LAvNFZvt9+8/XnAE/mMtYZBz8nPe7RPBcMa11ztXyk2m/ZzM9s7FfWOtp+bKRX9u3+s3pquIZ9p5Y5nRL2DwSD+kZExbxqeyTJrJGuvUqoWyAJOm+1Ta307cDvA0tVr9PlLSvnIJUvGXj+jqoD3XLiYxh4Xb6tdZFLI5irJdbCsIo/PX7Mq1aGYqiI/i4ntPRcV5jj4tyuWseNEH9evmc/T37OzvrqI9YuKTYvhfRctxmG1UJLn4OLlZWPLb750KXfVNVFTmssZVQWmxZNJZtN+bmZ7p6Le0fZzMxXmmN+/v//ms/nKffspL8jiI5ctM63c8Yyot81m45bXnsGDu1p46wbzR+dmypS5C5VShcAvgHzgZq11R7R1nflFOqekiiy7JS0mf0yWvYfqycR6HzxyjOLK+WTZLBTlpNc0GvHQGrqGfWitcVgtDHS1UlNTY2oMw74gLl/kF2ZxrgOH1fiB7NlQ764h39jozbzCrKhTDhmpsbHR9HrPBpl6XMvUehuxnzf3ufEHNUrB4tIcLGp2zFc43vbt27XWetID6qybILq0ZrV+9X/dwcrKfL71xrNTHY5pSmtWk4n1rl51Dp/5+T9ZWp7LDesWpDocw/iDYX7z3DH8wTALi7P5/sduNH0C2Rfru9nS0AvA289bxHwTDv6zod6/2XyUpw50AvDF153B+mrzRhdGZerE2Jl6XMvUehuxn3/hnl2c6HHjsFn4v3dtSNnk11NJqwmi5xU4eeP6Bbz2zHmpDsVUmVrv4hwHl68qZ/W8uX2aymGz8NYNC2nq83DGvHy+n4IYLlhaSp7TRl6WzZQEC2ZHvd938RJKch2U5WWlJMHKZJl6XMvUehvhC69ZxVMHOllXXTgrE6zpzLqIsx1Wbjq/OtVhmC5T6223Ks7NkC++igInFQXOlJVvtSjWLCwyvdxU19ths/DmDXPzWs7ZLlOPa5labyNUFDh55wXp+1nKE9+FEEIIIQwgSZYQQgghhAEkyRJCCCGEMIAkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYwNQkSyl1j1KqxswyhRBCCCFSwbTnZCmlXg0kdRp2jz/EPdubGPIFef2a+SwqMW42eZEcWmse3tPGsS4Xr1pemupwTCP1Nr7enUNe7nulBavFwls2LKQw225oeWLu2X68lxfqe1hanst151ShZuEULrPJXDiu7WzqZ/PhLhaX5vCGtfOT3uZxj2QppRYqpQqVUvkj/36Xivh/SqnrorzHDnwA+GOU129WStUppeq6urpijqW5z033sB9fIMzB9qF4qyJSwBMIcaRjmFBYs7dlMNXhmEbqbXy96zuGcflCDHoCNHa7DC1LzE17mgcIhTVHOobxBEKpDmfWmwvHtT0tkTY/1uViaGRu12SKK8lSSt0AXAEsBf45svh9wEeAy4HGKG/9BPAzYNK9Vmt9u9a6VmtdW15eHnM8i0pyqCxwkuOwsroqP+b3idTJtkfaymGzsGZhYarDMY3U2/h6r6jMJ99poyTXwZLyXEPLEnPT2kVFOGwWVlflk223pjqcWW8uHNfWLizEYbOwsjKf/Kzkn9yLd4tVwEeB/wKUUqp6ZBtbgJuA7cC+Sd63HlgMXAjkAZ9PNODxnHZrWj9uPxMppbj27KpUh2E6qbfxyvOz+PClS00pS8xN66uLZX7LOMyF49qahUWGTjcWb5J1J5Ek6d+AlcA3gBrgOPC7aNvTWr8XQCn1deAPCUUqhBBCCJFG4r0m61vAMuBrwF6t9QeAw0RGqcqAzVO9WWv9da11YwJxCiGEEEKklXiTrFuAfwEDwN0jy+xEkqzXA+9KXmhCCCGEEOkr3tOF84CbgY2ASyn1XuCE1voB4AGl1OokxyeEEEIIkZbiTbL+CHiAtwHzAStwWCn1AJFRsVzgyqRGKIQQQgiRhuJNst4IfBWoJ3LBez9wPfB7rfU/khmYEEIIIUQ6izfJ8gEPAieAhVrrF5RSPwE+rZSya60DyQ5QCCGEECIdxXXhu9Y6oLV+Tmt9XGv9wsjiINCbrAQrEAqzrbEXt//UJ682drvYfrwXX3BuPoXX7Q/xx5ca6R72pjoUU0Vr77mofcDL1oZehryp+S0SDIXZcaKPIx2nzo7gC4bYfrzXlKekZ+p+nqmkvc3j9Qf529YTPLW/PdWhJN3hjiHufLGRhq7hVIcSt0Sm1Xl65L//PrJIAzcmK6A+d4Dnj3Tz+L6TO0qvy899O1vYfLibTYdin3YnnXQMenl4dxs/fvJIqkMx1WTtPRf5g2H+uaOZF+q7eXRPaur68rFeNh3q4qHdbTT3uceWbzzUxebD3dy3s4Vel9/QGDJ1P89U0t7m+cOLx7nvlRZ+81wDdY29qQ4nqb736AEe2dPG9x87mOpQ4hZ3kgWM/iS5AUBrrYFw0iIaYRk3SaMCFJF/z/UJOy1zvH7RZFK9U1XV8eWqCf0r8l+FWaFlUnsLaW8zROvfc0E6f/8nMlHP6Pm68ec8dBJiAaA4x85lK8s4s+rkPEjFuQ5uXL+AbpePs+en5/xI05lX4OQN6+bzurPSe4qCeE3W3nORw2bhLRsW0tzn5ox5BXw/BTFcuLSU3Cwb+U4bC4qyx5ZfvqqcsvwsynKzKM51GBpDpu7nmUra2zwfeNUSinMdVORnsWHx3Joa6EvXreal+m4uXRn73MazRSJJ1nyl1IvAqpH/KmClUuplIFdrfc5MArJbLWxYXHLa8urSHKpLc2ay6Vkt22HlXRcsTnUYpovW3nNRZYGTygJnysq3WhTrFhWdtjzLZuVck+Zry9T9PFNJe5vHYbPwttpFqQ7DEMvK81hWnpfqMBKSSJLVqrV+g1LqQa316wHG/y2EEEIIIeK4JksptVQp9TBQMG6ZUkpZSOLpQiGEEEKIuSCeC9+bgXuA5UqpW4g8kPQp4AkjAhNCCCGESGcxJ1laa7/W+vfAeuBqYEBrfTXwWiKTRAshhBBCiBFxX5Olte5SSl0HLBhdBNyf1KiEEEIIIdJcIs/JQmsdBM4d+Tustf5VUqMSQgghhEhzCSVZI74y/h9KKZtS6rOTraiUukAptUkptVkpJffzCiGEEGLOm0mS5Zvw7zDwtijrrgReB/wQuHYGZQohhBBCpIWZJFmnPLZBax2euGzca3/SWruBWmDXxNeVUjcrpeqUUnVdXXNzbkIhhBBCZJa4LnwfmRQ6SOQp7xVKqZvHv8wUz8tSSq0EFmmtX574mtb6duB2gNraWnnmlhBCCCHSXrx3FxZx8jShDciP5U1KqRzgVuCDcZYnhBBCCJGW4kqytNbfGf1bKfUWrfUPx7+ulHpzlLf+F1AB3KqUekRr/a+4IxVCCCGESCOJzF046pTTeiPT61gnXVHrrzDhbkQhhBBCiLlsJhe+Z034txV4egbbE0IIIYSYM+JOspRSHx7583sTXsoFts44IiGEEEKIOSCRkax3A2it7x6ZKHqUG/ifpEQlhBBCCJHm4kqylFJ5I//NGVn0XaXUspG/A0AoibEJIYQQQqStmJMspZQT2ASsA+4ZWdwJfH7k71ygP4mxCSGEEEKkrZiTLK21V2u9AXgF+KRS6u3AXqBcKbWQyNQ5x4wJUwghhBAivST6CIdLgZ1EHuPwfeC/gUbk7kIhhBBCCCD+a7J+B6wAztRa7wTQWm8DFgNvBe5OdoBCCCGEEOko3rsL/w9oA/488u/Rh49+CxjWWnuTFZgQQgghRDqLK8nSWtcBbq31bqWUFdgzsvxF4Cml1N8NiFEIIYQQIu0kck3WnQBa6xDw6dGFWutvKaUWJSkuIYQQQoi0FvfDSLXWd0zxWtPMwhFCCCGEmBtmMnehEEIIIYSIQpIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQxgSpKllKpUSj2mlLpbKVVkRplCCCGEEKmU6LQ68foCcAuQBbwH+Fm0Ffe3DrL8S49w/pIS/vqRCwFoH3Bz3W0v4PIHeduGRXzzjWcD0O/289X79zHoDfCJK5dTW1OSUHDNfW4e2t1GXpaNt2xYiNNunf5Nk9ja0MvWhh5WzSvgmjMrp1xXa81je9s52jXMxcvL2NMyQM0XH8ZmgfrvXJdQ+elob8sAS774MMsq8njqs5enOhzDeL1e1v3Ps/gCYRYWZ3OkY5h133iC69dU8e0bz0lqWQfbBvnfJw5ht1r40nWrWVScA8CfX2rkR08eJtth5S8fvoCasjwAvnrfXu7a3kSew8ZD//Eq5hXmTLn9ePbziRq6XVz+g2d5y7kL+eTVK6Zct7F7mHf9dgsef4jPXrOSd19UM+X60eodjZH1nti/M1Wi+/mxrmE++bcd+AJhvnzdaq48I/b97GdPH+aXG4+SZbPwj5svZFVVYczv/f5jB3lkdysrKvP59bvPxWqN7btgYnsb2b+jeeftL/HisV4AfvWudVx7zgLDywyHw3z30YMcaBvkujXz6fcE+MWz9Vy+spyzF8T+uUdT3znE4/s6uH3jEQZ8YbLtFq45s5JBb5A1Cwv56GXLUAq+8cA+2ga8vO/iGq5eHd8xCeC5I13saupnzcIiLltZPu368fRvs04XVhF5cOluYMlUK4a0Jqw1r5zoH1t2/yutDHkDhMOaJ/a3jy2va+ylY9CLxx/i2UNdCQd3oG0Ijz9E15CP5j53wtvZ1dRPIKTZ2zJAIBSecl1PIMTB9iECIc2e5oGx5cGp3zbn6JH/NXQNpzoUQ22u78UbCKOBln4P/lCYUDjMxhnst9E8d6SLIW+QXpefLUd7xpbfu7OVQCjMoCfAP3e0jC1/Yn874bBm0Bvg/ldap91+PPv5RL5gmGAozNMHOqdd957tzQx6AgRCYe7dOX1c0eodjZH1jta/M02i+/lj+9rodwfwBEI8sKstrvfet7OVUFjj9of448sn4nrvxkOdBMOaA22DtA7EPoHJxPY2sn9Hs7Wxd+zv/33isCll9rr97G4eIBDSPHekC18ghD8YZneS9vm9LYMMu70M+CL9zRMIs/1EPx5/iMMdwxzvcXOofYjGHje+YJhnD01/XJnMaN/e1dQf0/rx9G+zkiyv1loTmYYnb+KLSqmblVJ1Sqk67RlEKcW6RUVjr9+wfj75TjsWpU7JUs+tLqGiIAun3crlKxP/tXjGvHycditl+VksKJr61+xUzllYiM2iOHN+AXbr1B9ttt3Kysp8bBZ1SsZvy7Cr5NTIf5eUnbZbzCmXLS/BOdK48wudOKwWLBbF5TH8aorXq1aUkZtlpSjbzgXLSseWv2HtPGxWC/lOO29cd/JX7tWrK7EoRb7Tzg3r50+7/Xj284mybBasFsVVZ1RMu+6N6xeS77Rjs1p4w9p5064frd7RGFnvaP070yS6n197VhWF2XacdivXr5m+7cd7/dp5WC2KbLuVd55fHdd7L19RjtWiWDWvgPmFzpjfN7G9jezf0WxYXDT2939cNfUocbKU5Dg4e0GkX1yyvJwsmxWbRXFOkvb5s+YXkJfjpDAr0t+cNgvnLioky25heXkei0tzWDUvn+qSHBxWS8Kf95qFRZG4F8YWdzz9W0VyH2MppT4DPEfkdOFVWutvRVu3rKxM19TUnLJMa+hx+QiFNQVOO9mOxE7nzWaH6o9RVDGfvCwbuVlmncVNvcbGRia291zV5/LjD4XJzbLR3dZsWL39oTD97gAKKM51YLOoad9jlnjaO6w1vS4/4TAU5tjJSuNfINK/M0sq6u0LhhlwB7BYoCTXgUWZ3+/NqLc/GKbfEzm+leQ6sM6C49v27du11nrSA5RZvf0O4BdAPnDzVCvW1NRQV1d3yrLWfg//2BZ5mPzS8lxuWGf8uWazLVp5Np/9xb+oLHDyzgvi+/WVzmpra09r77nI7Q/y603HgMiB4Wf/8RbD6v1ifTdbGiKnDq46o4K140aFUy2e9q7vHOLBkVNF5ywo5NVxXv81m0j/ziypqPeT+zvY2xI5dfX6tVUsr8g3tXwwp94bD3WOXU507dnzWF1VYGh5sVBK7Yj2milJltZ6AHh3ou+fV+BkdVU+XUO+hC9un+1yHDZKch1csHRu1i/T5Ths1NYUc6zLxYVLS6Pf+ZEEZy0o5ESvG5vVwspK8w+0ybK4NJel5bkMeYOzKlFMhPRvYbR1i4roGPSS77SxuDQ31eEYZs3CIlr7vWQ7LCwtn/31TItxa4tFce3ZVakOw1D5Thvvu7gm1WEIA126opxLVxh/jUZhtp13xHktymxkt1rmzKi19G9htPL8LN594eJUh2G4klxHWo0Gp+9FDkIIIYQQs5gkWUIIIYQQBpAkSwghhBDCAJJkCSGEEEIYQJIsIYQQQggDSJIlhBBCCGEASbKEEEIIIQwgSZYQQgghhAEkyRJCCCGEMIAkWUIIIYQQBkjrJMvvD/G5u3bywT9so75jKNXhxG1P8wD372yhpd9D+6CX9/xuC88e7Eh1WIYbX2+3P8T9O1toH/CmOizD3fbUEd792y08trd9VtX7aNcwX3tgH3c832DI9se3t5G8gRCP7W3n6QMdBELhadc3s97p3L+P97i4f2cL+1sHUx1K2khF/x7yBnh4dxubDncRDmvTyr13RwtfuW8v24/3mVLei0e7eXBXK30u/9iyusZe7t/ZQteQL+7ttQ14uH9nC7ua+uN6X6zHtbSYuzCaf+5s5uVjPQDc9swRbrvp3BRHFDtvIMTTBzvQGgY9AfrdAY51DXPb0/VceUZlqsMzzMR6D3kDHOty4fGH5sR8e9G0D3i4a3sTWmt+/syRWVXvP750nINtgxxsG6S2ppg1C4uStu2J7W2knU39HGiLJAIV+U7OWVg45fpm1jud+/dTBzoZ9ARo7HazsjIPmzWtf5ubIhX9e2tDL4dHBhsWFmezrDzP8DL73X7+UXcCreH3LxjzY2W81n4PW471AqAUXL9mPj3DPp470g1AMKR584aFcW3z2YNddAx6OdblYllFHnlZ06dF8RzX0rq3rKoswGZRACwvz09xNPGxWy0UZtuByMSeI9VgYXF2CqMy3sR6W0cqXp6flcqwDFeY7RjrvFVFzllV7+qRfc5ptzKvwJnUbU9sbyOV5WWhFFiUojTPMe36ZtY7nfv3aLuV5NrH9lsxtVT079Gy7FZFcc70+38y5DhsFI3s5wuKktuHJlOQbSfLHklbyvMi9c3NspGbZY0sS+DzLhs5VhRk28myxZYSxXVc01on/D/gU+P+zgfeMZPtaa3ZsGGDjsfBtgH93JHOuN4zW3j8Qd3S59ahUFivWbdeP7W/TQeDwVSHZbjx9T733A26td+tw+FwqsMyXMeAWz+xt037fMFZV+9dTX26Y8BjyLbHt3e8/Tte3UNe3efyxby+WfVO5/4dDIV1S59bewPxx250e89WqerfHYMePeDxm1pmv8undxzv1YFAyJT2HvIGdPuEPuvyBRL+vEMj+7fHH9/+Pb5/A3U6Sk4T9+lCpdRrgItG/vlm4Kcjf/8I2B3v9mZq1bwCVpldaJI47VbmF0V+2dqtFq5ePS/FEZljfL2VgqrC9Pt1n4iKgmyuOWt21juZp8omGt/eRivNi++XrFn1Tuf+bbUo09pvrkhV/67IN340aaLCHAfrq80ZOQPIy7Kddkovx2Ejx5HY1U+WBPfvWI9riUR1BfAkEAauUEo5iSRaJ7TWP0tge0IIIYQQc07M12QppexKqTcAS4EswAnkEkm4/q61/tYU771QKfWoUupXSqnzZxq0EEIIIcRsF89IVh6wGigDzgY0kAOcAD6olNqtte6J8t51wC1a6z0ziFUIIYQQIm3EnGRprfuA7yulKoBfaa2HlVKXa63fopRaB9yrlHq71rptkrevB85XSpUCH9daNycleiGEEEKIWSqRRzh8DfjiyN+7lVLvIXL68D+A70R5zxe01h8Evgu8f+KLSqmblVJ1Sqm6rq6uBEISQgghhJhd4rrwXSn1LSAAvEkp5R/5+2LgVVrrjymlPhLlrTcBvwZKiFwwfwqt9e3A7QC1tbXmPapWCCGEEMIg8d5d+CjgA64BHh63/BdKqWytdbTny3uVUvcDIeCj8YcphBBCCJFe4kqytNYvAiilfqa13j66XCn1QyIXwU+aZGmt7wTunEGcQgghhBBpJaFpdbTWd034992AWyn1jqREJYQQQgiR5uJKspRSk04QqJSaBzwGzHiGZn8ozNMHOvD6g6cs73X5Odo1bNjs4o3dLjqHZjZjui8Yor5zCJcvOP3KEwx5A3z53j10D7lnFEO6cfmD/PCJQ/S7zJutPlWGvAHqO4cIhMJR93OjTbafh8Oao13D9I6b1X4qM9nPU1XvyQSDYTYe6uRo13BM60v/FrEKhTX1nUMEQ6ddgmyoe7Y38Xy9uTePjT+uJdvosam+Y5inD3TQ7578GNU97ONY1/DoFH9x8wYifdvjD80k3EnFe03W/UqpG7XWA0qpPCLT61wHXAp8f+IIVyJa+zzcvvkYLx/r4cvXnQnAoDfAX7ccJxDSbFhczGUry2dazCnqGnt57kg3FqW46YJFCU9N8OCuNpp63RRk2/ngq2pQKvbJVE/0urm7ronnjnSx+ZarEio/HR3vcfOHFxrYeKiTBz95aarDMUwwFOZvW0/g8oVYWp476X5utGj7+XP13ew43ofdqnjvxTUUOO1Tbmcm+3kq6h3NrzYf5bkj3ditiu+/ZQ0LinKmXF/6t4hVr8vPg7vaOGNePq87p8qUMv/nof08srcNpRTfe/M5XLI8ud+Tk5l4XEu20WPTo3vbyHfaqC7J5cdvX3fKOn0uP3/bcoJgWHPBkhIuXl4Wdzn3bG+ma8hHWX4W77lwcZKij4j3dOELwENKqXuAXwHnA3cBVwMLlVI/mmlAowNV/Z7A2DJvIEQgFHlhyJv8X8BDI79Mw1rj9iWeyQ55IzG7fUHiHXAbTcBdMyg/LY3U24h2nU2CYY3HH/mlNzxu/xi/nxst2n4+PPLZB0Iab2D6/W8m+3kq6h1NnzsSQyCkGYghHunfIlbhkQYfSmDUM1FdLh8AWmvaB8w5MzDxuJZsQ94A4XAYbyBMWMPgJP3UEwgRDM/s8x6NfdiA76F4R7JeAnxa62+PX6iU+iDwauBTMw2oLM/BeTUl3HjugrFlFflOrl5dQdeQj/OXlMy0iNNctLQUgPwsGzVliWfjrzu7ij0tAyyvyMNqif1XLkBRjoPqkhw+dfWKhMtPRyW5DlbNK+ATVy5PdSiGctqtXLdmHg3dbtYtKpp0PzdatP38spVlOO0WKvKdMY3izmQ/T0W9o/nwJUv429YTLCnL48yqwmnXl/4tYlWY7eCcBYWcu7jYtDK/eO0ZfO+xg5TnZvGWDYtMKXPice3HSd7+5SvLybZbqSrMoWXAw1VnVJy2zvyibK5YVU6f288FS0oTKuf6NVUcaBvijHmTXhE1Iyqec5hKqSXARmAHY2MQeIFeoAjYr7WO9kDSmNTW1uq6urqZbCIt1dbWIvXOHFLvzCL1zixS78yilNquta6d7LV4R7JagcNa6xvHbTwHqADOGfmvEEIIIUTGi/c5WT6l1Eql1P8AfiIjWG3ADq31g0YEKIQQQgiRjuIdyQK4mciT2+2AE1gJ3KCUOgN4ALhVa+1LXohCCCGEEOkn7iRLa/34ZMtHnvreKwmWEEIIIUQCT3xXSp2vlDqmlHpGKfWsUuoVpVQNsBl4XdIjFEIIIYRIQ4lMq7MDeEVrfZXW+krgJ0CN1vp+IPlPIxNCCCGESENxnS5USr0AuIFzlFLPEZkQWgEOpdRHOPlYByGEEEKIjBbv3YWvUkrZgXcCJVrrsWePKaXMefqZEEIIIUQaiHckywY8SOTxDZcrpa7i5OhVCPhlcsMTQgghhEhP8Y5kBYFrlVLfBJ4Dfq+17lZKfQ/4ida63YgghRBCCCHSTcwXviul1iulfqaUug34ALAI+OrIvy8GbpxyAzEa8AT45cZ69rUOjC3z+oP81z938/7fb2XToc5kFDPrNHa7uOJ/n+WXG4+kOhRTTdbec9V3HznAe+/Ywl11TYaW0z3s43fPN/DHlxoZ9J6cULWl38NvNh/jb1tPnDIR9NGuYX616Sj/3N5MMBQ2NLZ4tA94eOMvXuDVP9zEY3vT+/dbpvZvYZ7H9rbz6h9u4o2/eIH2AU+qw0mqnz9zhGt/spkP/n5r2n1XxHN34QngL8CdQMfIf0f/90XgLckIyBsI4QuE2dV08oM80D7EsW4XHn+Ipw7MzSTLGwwTCIZ5Yu/crF80k7X3XNQ97GVnUz++QJiNBv9QONw+xKAnQM+wn4Yu19jy/a2DDPuCtA94ae5zjy3f2zKAxx/iRK+bzqHZ85i7J/a10zXkxRMI8sCu1lSHMyOZ2r+FeR7Y1YonEKRryMuT+ztSHU7ShMNhNh3qYsgboL5rOO2+K2JOsrTWPVrrl7XW24HvEJlKZ/vI/14E/jcZAWXZLFgtirPmF4wtW1GRz4KibGwWxaUrypJRzKyTZbNgUYrLVs7N+kUzWXvPRSU5DlZXFWBRiouXJjZTfKyWV+SR7bCS77RRU3ryqSqrKvNx2CyU5jmYX5Q9tnx1VQE2i6Kq0ElZXpahscXjyjMqKMqxY7daeN1ZlakOZ0YytX8L87z2zErsVgtFOXauWFWe6nCSxmKxcOHSErLtNhaV5KTdd4XSenY9daG2tlZv27YNpdRpr4XDYSyWRB7tNfvV1tayZcsWrFZrqkMxVW1tLdHaey4a3YdTNVu91nrSzzra8mRLpN6hUCjt+0Um9+9U7Oeplqp6p7qvGFnvcDiMUmpWflcopbZrrWsney2RuQsNF+1DnKsJ1qhMOwCPmo2dxiip3oejfdazuQ3mSr+YK/UQs9dc3sdSfexMVHpGLYQQQggxy0mSJYQQQghhAEmyhBBCCCEMIEmWEEIIIYQBJMkSQgghhDCAJFlCCCGEEAaQJEsIIYQQwgCSZAkhhBBCGMCUJEspVamUekwpdbdSqsiMMoUQQgghUsmskawvALcAtwLvmWrFpj43l936DA+80nLK8kd2t/HLjfV0D3uNizKFjnW5WPv1x7njuWOpDsVUnUNe3vXbl9lytCfVoRjuV5vqeedvXmbz4U58wTCbDncx4A6YVn5bv4cP/H4r/3nPrlOWdw56+eXGeh7d22ZIuY3dLjYe6qTP5afX5edzd+1kf0vyJ3n1+oPc8XwDf9lynHA4PLZ8yBvg15uOck9d0ynru3xBnjvSxcH2waTHMtFs2s+Nbu/nj3Rx29NHaOgaNmT76SAV7R2tf5vJiOPa0a5hPnLnNl79w41895H9ph4zk8GsJKsK2APsBpZMtWK/O0Bzn4cv3bdnbNnBtkHufKmRTYe6+PWmuZmEuPxBBrxBvvPogVSHYqruIT+7m/r54r17pl85jTX1DvPLZ4+yp7mfL9+7lwF3gB3H+3jyQIdpMXz+nl3UNfbyyJ42fjcumf/Nc8fYdKiLP7zQyMG25CYc3kCIB3a18sqJfh7b107nkI+Xj/Xw9Yf2J7UcgHu2N/P4vnYe2NnKI3vax5bf+WIjzxzs5O7tzbw87ktv8+Eu6hr7eHRPO11DvqTHM95s2s+NbO8Bt59fbjzKC/Xd3PZMfVK3nU5S0d7R+reZjDiu/ezpep4+2MnRLhd/fOm4qcfMZDArycoHNgJPA/MmvqiUulkpVaeUqgu7I79w7daToeU6bFgtkbnVcrNm5XSLSWOdxXPIGWKkutn2uX15YLbDhm1kH3barYw2s9PEeueP9B2lFKV5jrHlo33KZlHkOpPbv6wWhcMWqaPTbhmrd44j+XOs5TntY38X5pz8O3es3pA3rn5OeyQG27gYDTOL9nMj29tus4x9lka0cdpIQXtH699mMuK4luOwjCUqVosy9ZiZDEprbXwhSv0Z+D/gEuBirfUN0dYtKyvTNTU1MW972BckFNbkO21YUpyg+INh3IEQTptl7AA+FU8ghC8YJtdhpaGhkbyyKkpyHTG9N51JvTO33i1NJ4inf8dD68ipQaUUeU4byT4aZGr/jrfe4zU2NhrW3tGEtWbIG8RqUeTF+aPc7Q/R7/aT47BRNC5Rj1cq6j0bpGO9AyHNgMdPWENhtp2sGH9wje/fu3e+orXWk77RrGGhjwO/AM4DPj3VijU1NdTV1cW00aNdwzywsxWAtYsKueqMyplFOUO/e76BQU8Ai1J8/Mpl2KzRG8sbCPGrTUfRGsrys/jU26/l0i/8loXF2fzwbevMC9pkUu/MrvdPP/HmmPt3vF4+1sNLI6cDX726knMWFiZ1+5nav+Op90S1tbWGtXc0zxzsYFdT5IzIDevms7Q8L+b3fuKvO+ga8qEU/OrdGyjKSWxEKBX1ng3Ssd5/3XKCh3a3EgprLlxaymeuWTnteyb27/deVLMj2rqmjLtprQeAbwIvaa0fnfj6+NOFXV1dMW+3KNs+dgqmNDcrWeEmrGxkiLY41z52ejMau9VCwcjpjbJcB6PHrarCbENjTDWpd2bX20ilI9u3KEWJAadLMrV/x1Pv2WD0u8BmURRmxzcaNa/QCUCh006OY25fmiIiSvMc5DisOGwWKvJjyyPiOq5prQ3/H5AD3AeUTLfuhg0bdDz6XX7d1u+J6z1GCQRD+kSPS3v8wZjW9/iDuqnXpYOhsF67/ly9raFHBwIhg6NMPal35tY73v4dr85Br+4Z9hmy7Uzt3/HWezyj2zuatn6P7nf5436fLxDS2xp6dJ9rZvtQquqdaulY71AorI91Dekj7YPaF0f/HN+/gTodJacxK1X/L6ACuFUp9YjW+l/J2nBhjp1CEj93nkw2q4VFJTkxr++0W1lYHFnfZlHU1pQYFdqsIvXO3HobrTzGX6KJyNT+HW+9Z4PREal4OWyWtG0nkRiLRbGkLPZTyqNiPa6ZkmRprb8CfMWMsoQQQgghZoP0uhdSCCGEECJNSJIlhBBCCGEASbKEEEIIIQwgSZYQQgghhAEkyRJCCCGEMIAkWUIIIYQQBkjoEQ5KqWeBiZMeKkBrra+acVRCCCGEEGkuoSRLa31lsgMRQgghhJhL5HShEEIIIYQBZvTEd6XUfwDrRv9J5HThB2calBBCCCFEupvptDo3Ae8Ewpx+jZYQQgghRMaaaZLVATwFHGdkJAuQC9+FEEIIkfFmmmTZgXO01u5kBCOEEEIIMVfMNMmqBLYppTpGF8gjHIQQQgghZpBkKaWWAT8nkmhZgAbg4STFJYQQQgiR1hJ6hINS6kvAZ4FBYAdwEDgDeFkpVZG88IQQQggh0lOiI1n/T2t9yYRl9yql8oHLgHtmFpYQQgghRHpLNMk6qJS6A7gLaAGygUuJ3Fn4zSTFJoQQQgiRthKdVufDSqkbgdcBOcAwsB24TGs9lMT4hBBCCCHS0kzuLtwNFHLywvdgUiISQgghhJgDZnLh+2eAIeTCdyGEEEKI08iF70IIIYQQBjDtwnel1HuBYq31TxMsUwghhBAibSTjwvdcIqcNo174rpRaC1xB5DquKdV3DnPuN5/ghnXz+dobzgagfWCY1/z4BTyBEG9ct4AfvHUtAN5AiPt3tjDkDXLdmiqqCrMTqQ5tAx4e3t1GvtPGDesW4LRbE9rOTOxpGaDmiw9TnGPjla++1vTyU+VQ+xCrv/Iorzmzkp/edG6qwzHUum88zqA3yOqqfAY8AX616SiXrSjnzPkFSS2nvmOIHz55GJtV8aXXraaqKNIv7q47wa2PHSLbYeUvHz6fRSV5AHz7of38desJ8p02Hv7kxZTl5yQ1nlNim6R/R9PUO8y7frsVjz/ELdeu4q211VNvO0q9o3nuSBe/f6GBqsJsvnLdapyOmU6AEd1o/15Q5OSFL15tWDmxMLPe8bT3eE29Lj751514AyH++/WruWR5eczvffpAO99/9BB5Thu/eve5VBTE/r2wu7mfF4/2sKw8j2vOrIz5fQBP7u/gaNcwFy8rjet9Ynrrvv44/d4guQ4rv35PLYc6hsi2W7lx/QIsCr750H7aBjy8/6IaLls1u65YSvSarFIidxR+GfgP4CWgI9qdhVrrXcAfY9m2LxgmEArz5P7OsWV/erGJYV+QUFjzxP72seVNvW5a+70MeYPsbRlMpCoA7G0ZZMgbpLXfS3Nfaqdh7HNn1v0DgVCYUFiz6XB3qkMx1KZDHfR7goQ1HGofxhsI4fGH2NnUn/Synj3USa/LT+egjxfqT36uf9/WjDcQos/l5666lrHlD+1uJRgK0+fy88/trUmPZ7zJ+nc0/9jWTJ/LjzcQ4u/bmqddP1q9o3liXwcuX4j6zmEOtJtzU3RLv9eUcqZiZr3jae/xHtzdStewlyFfgH9ub5n+DeP8c0crLn+QjkEvj+/rmP4N4+w43ofHH2JvywBuf+zHYrc/yN6WATz+EK+c6I+rTDE1r9dLvzfSFi5/iCf3dzDoCdAx6KWh28X+tkHqO4dx+UI8cSC+9jZD3EnWSIL1LPAa4CHgfuBs4PVKqZ8nEoRS6malVJ1Sqk57BlFKccHSkrHX31q7iCybBaUUFy45+SthQXE2RTl27FbFysq8RIoGYGVlHnaroijHzvxpfv0aLceeUN6btqwWhVKK9dVFqQ7FUBfWFJFtt6AULCh24rBF/l5dlZ/0si5aVobTbiUvy8Z5S072o2vPqsRqUWQ7bLx+TdXY8kuWl6GUIsdh5fo185Mez3h2qzqtf0fzhrXzyXbYsFoU1541/ahCtHpHc/HyUqwWxbxCJysqkt8OkynJsZtSzlTMrHc87T3e1WdUkuuwYbdaeO1Z8+J67zWrK7BZFPlOO1esin0EDODM+YUoBUvLc8mO44xGtt3K0vLckT6d3JHpTOd0Osm2KQCybBYuXl6Cw2ahINvOopJsVlbkUVXoxKoUr1pWluJoT6e01vG9QalXA+dprb+rlLoMeJvW+hMjr23UWl8R5X1XAOu01j+Zavu1tbX6+edfxuk8fQh72OUnL9dx2nKtNUqpuOoxUTissVhmto2ZqK2t5aEnNzKvOPFkMR3V1tayefNL5MyCLx8zDA77KMjLora2lq1btxm2z4XDYQAsllOTdq83OGnfcrsDprRBbW0t0fp3NMFgEJsttvWj1Tv6tsPYbMb/sKmtreWxpzdTVmjcqdh4mFnveNt7vFAohNUa/+Ubfn8IhyOxyz5m8l0w+t7a2lrq6uoS2kY6M7Le/UNeCnKzsFjUpN/5Zu3Tk1FKbdda1072WiJ7/nbgm0qpp7XWm4HNI4W8B/AnHuZJ0TrkZAnWSNkzLjOVCdaoTEuwRmVKggVQkJc19reR+1y0JCNa3zKzDeL9wo01wYLYk6uT2zbvoDxbEiwwt96JJlhAQgkWkHCCBTPrl7Phe2SuKsp3jv092Xd+qhKs6cS992ut+5RSbwLOmvBSFfC2Kd63EdgYb3lCCCGEEOko0bsL24F2AKXUaqBEa31rMgMTQgghhEhncSdZSql7iTy2oQQoAj4EVCulygAn4NBa/ymZQQohhBBCpJu4TmIqpVYCFcBNwN3AAUADYeCLQB7wpSTHKIQQQgiRduK9UuwsYAmRxIpx/wXwaK1/x8hpRCGEEEKITBbX6UKt9b1KqbcAfycyajV7bpcRQgghhJhF4kqyVOS+yT7gO0RGsb428tL4ZyvE9+AtIYQQQog5KN6RLD3yUNE+IASsABTw34BVKfVOYJ5SyqK1Dic5ViGEEEKItJHIIxysRO4i1ERGsCzAN4CrgXcAPyKSeAkhhBBCZKxEThceAu4ickfhWqB65OUPE3n6+/Na61AygxRCCCGESDdxny4E3qiUWgKsBL4FNADZWuuAUurfgdk3DbYQQgghhMniHcmyEHkelhfIBnYQSao+rpQqHLfNryYzSCGEEEKIdBPXc7JGLma/eOSf9wDvAT4I3EAkuRoArklmgEIIIYQQ6SiRaavHP4i0ishF7i5gJ/Ay0JWUyIQQQggh0lgidxeuBOxADZHEqjWJ8QghhBBCzAmJJFk9wFFgG+ADepMakRBCCCHEHJDI6cLRJGsr8DEi8xnKU96FEEIIIcZJJMmyjvxXA7uAYSCLyClEJ6dOsSOEEEIIkZESeYTD34Fy4CvAVq31X5VSYeAJIonX/yU9SiGEEEKINBPvw0jDwB+VUkVAMeAfWf73cavdn7TohBBCCCHSVCIXvqO17gf6kxqJEEIIIcQcksg1WUIIIYQQYhqSZAkhhBBCGMCUJEspVamUekwpdffI9VxRBYJhHtzZEvO2Pf4QA57ATENMOX8wzN+2HE91GKYLhTU7jvelOgxTDHj81DX0EAqF0Bp6XX7TY2jqc9M7fHq5/W4/vmAopm1orekZ9hEMhWNaPxAK0zPsQ2s96+q9p7mfnmGf4eUHQ5qNBzsNL2c2cPuD1HcMEQ6HU1bvAU8Ajz+2/Xm80X07EOO+LU5lRP/uHfayv22AfrefXpcfbyBEv9v8Y0iiEromKwFfAG4h8qiH9wA/i7bi4c5hPnf3Ln61+RgP/8elU250wB3gL1uP4w+Gee1Z81hdVZDUoM10uGOIL9+3l19tOsamW65MdTimOdbt4pN/28FFy8r437euTXU4hvH7Q7znt1vpcfk4t7qYXpefO19sZMPiYi5bWW5KDI/sbuOPLzfisFn4nxvOYVFpDgDbj/ey+XA3eVk23n3hYrId1im38/i+dg60DVFV6OQd51dPuW44rPnHtia6hnysWVg4q+r9nUcO8PDuVrLtNu784PksKM42LIb6ziE++bcdrK0u5s8fusCwclItGAzzxX/uoWPQywVLSlJS7wNtgzy+rx2HzcK7zl9MYY495vc+c7CT3c0DlOdn8c7zq7FYlIGRzj3J7t/bj/fyvUcP0t7voSDbzqUryun3BKgscHL5qnLOrS5OQtTGMut0YRWwB9gNLJn4olLqZqVUnVKqLujqB6C13zPtRruGvfgCYbSGlr7p15/NRp/m2mXCr+rZJBSO1Ly+czjFkRiry+WnxxVp22PdwwTDkV/KLTHs58myv20QrcEXCHOk6+Tn3TzSd4Z9Qf5/e2ceJ1dRLf7v6e7Zl2QmM8lksi9kIWELCTsEEFEwiA/EFQURIS5P8Sn8RAVlc3ugPJ8LgiAIbog+wLCIrKLIkiAJIRsJ2TPJJDOT2Zdezu+Pup10JjOZnmRud8/0+X4++WT6dt1b5/Stqnvq1Klzd7f3PUOMl9/e1NGnN6szEmNns9N76+72jNJ7ZU0TAO3hCKu2N/oqQ9Tr4Bt3tfpaT7pp6oiwo6kDgHU7W9Ki99aG9j33u7/jabxt72zupDNi3qz+MtD9e0tDOw2tYSIxpa61i6aO8N7xZJA881PlyepQVRWRIFDc/UtVvRO4E6Bswgwtyc/h48eP6/OikyqKmVVdSnNHhLkTM9+iPRB5oQAFOUE+eOyYdIuSUkrzQ4weVsAlJ05Ityi+MqasgHNnj+bfm3fz4XnjuP6uEGOGF3DilBEpk+GiuWOpa+mkrCiXU6ZW7Dl+4uQRdEViVJbkUVWa3+d1Tp9eyZKNDUwbVUIoeOB5WkFukFMPq+Cdna3Mm1ROcV7m6H3laZP5n2feZmxZIaf77FUrzgsyojhvyLfz8uJczjtyNEu3NPL+o6t5MA16z51YRmN7mJL8EJMqivp17mnTKnltfT2TK4v69Oga+zPQ/fvEySN4c0sjq7Y3MW1UCdOrShAEETh+cvmA1OE3our/G3FE5MvAi7jlwjNV9abeylZUVOjEiRN9lyndKFDX0kk0puTnBKnZupmykdUU5AQpLUjevT3YWbX2HdM7i/TesGED1r/tfmcqje1hOsJRggGhojjvoK8z2PQ+WLq384YdW7NC7+4sWbJEVbXHGWeqPFn3AD8FSoArDlRw4sSJLF68OCVCpZOOcJRfvPAOMVUqS/K49pPv479++mfGDC/gQ/P69uINFcZNm216Z5Hec+fOtf5t9ztjuf/ljexq7iQgwpXzJ5Ofc3DerMGm98HSvZ3f/oULs0Lv7ojI6719lxIjS1UbgYtTUddgIT8nyHtmj2L9zlbmTCjjuwU5zBxdwrETBocLdKAoNb3TLYrhA9a/BydnHz6K1zc2MKmy6KANrGyiezu/Pd0CZSCp8mQZPTCjqpQZVW5HZEFOkPfOHp1miVKP6W0MVax/Dz5GleZzzhF2n/pDYjs39seSkRqGYRiGYfiAGVmGYRiGYRg+YEaWYRiGYRiGD5iRZRiGYRiG4QNmZBmGYRiGYfiAGVmGYRiGYRg+YEaWYRiGYRiGD5iRZRiGYRiG4QNmZA0g7+xs4aElW3hzS2O/z23tivDQki0D9vbywYLpnV16D2asfw8OGtvDPPLGVp5ZuYNozP9382Y7r29q4KElW9hU15ZuUTKSQZ3xvTMS5ZE3ttHSEeHcI0ZTNSw/rfI8u6qW5o4IWxramDm6hFAweRu2pSPC5vo2wtEYHz1uvI9Spp9nVu5g3c4WTppSYXpnmd5+Ut/axaJl2wgGhPOPHkNx3sAOb9nav/+9qYHXNtQzvaqU+dMq0y1On7y2vp53drYCMGFEEVNHFid97vKtjby0bhdTKot518xRfok4ZGjvinLvP9dT39rF2trmdIuTkfjiyRKRE0TkCRG5Q0SOF5EnReSPIjJ8IOvZVNfG1oZ2GtvDvLm1/7PLgWb0sAIAKkvyCAakX+fGy6fbUPSbtq4Iy7Y00toZZcnGBtM7y/T2k1U1TdS1dFHb1Mm62pYBv3629u/FGxpo7Yzy+sYGwtFYusXpk/hvnBsKUFGc269zF2+op7UzyrItjbR1RfwQb0gRicVoao8QjiqNbeF0i5OR+OXJOhq4RlXfFJFbgWuAPOATwP8OVCXVwwsYVpBDa2eEw/oxW/GLc2ZXMW9SGWWFuYj0bxAeUZTHxSdM6PegMNgoyAkysaKQDbvamFFVYnpnmd5+MrmymDe27CYUEMaXFw749bO1f88YXcLiDQ1MHVlMTj+8d+li9phhjBleQG4oQFE/vZkzRpfyr3V1TKwopMBeEN0nxXkh3ndkFau3t3DWzJH8Ld0CZSB+GVnHAMeJyAigGLgayAU+OpCVFOWF+NTJE4kp/Z5Z+kEgIIwsObiZqoibIQ91RIT/OGYs0ZgSDIjpPcTprrefVA3LZ+FpUxCh30ZQMmRr/z71sEpOmlKREWNsspQVHZwxe8LkEcybWD6odE0nIsJFc8enpH8PVvwysq5W1SYROQH4p6qqiARxBtd+iMgVwBUA48f3L15BRAjavR10ZGuHNL39JZClv6/fZFO7zSZdBwr7zXrHL99v3GNVDlwvInNx3q3NPRVW1TtVda6qzq2szPzASsMwDMMwjL7wy5PVISKPAFHgy8AtQAmet8owDMMwDGOo44uRpar3AfclHLrYj3oMwzAMwzAylczfKmIYhmEYhjEIScrIEpELxI/tOoZhGIZhGEOUZD1ZX1BVez+BYRiGYRhGkhwwJktEVgH1wCwReQlI9GYFgWdV9Ws+ymcYhmEYhjEoOaCRpaozAETkOVU9o/v3IrICMCPLMAzDMAyjG8kuFyqAiJwvIo+KSPy84/wRyzAMwzAMY3CTrJEVEJFcYC7wVVWNAajqwL+F1TAMwzAMYwjQq5ElIqUi8hnPuHoQCKvqdaq6JnXiHZiOcJTfvbqJu/7+Dlt3tx/0dWqbO7j/5Y08/O+tdEXS85b57Y0dfPq+13hyeU1a6k8XzR1h7v3netbWDn17/cnl2/nZ82t5Y/PujNJ73c4W7nhhHX9asoVIND3tPx38c+0uPvPrxXzrkeV0haO+1pWt/TsdHMp4/sbm3fzs+bU8uXy7T9L5x7Itu1l4/xKueWgpTe3hdItjeBzIk5UHjAZewCUTfUZEnkr494yIvJASKXthc30b2xs7aOmMsGJb00FfZ+nmRnY1d7J+Vyub6lsHUMLkaeuK0tIR4cnlO9JSf7po64rS0Bbm1fX16RbFV9q6IqysaaIzHGPp5t0ZpffyrY20d0XZVN9GbXNnusVJGU8u305Te5hV25t5q+bgx49kyNb+nQ4OZTx/Y1MDneEYK2uaaOuK+CShP/z1rR00tHWxsa6N1zakf1wxHL0aWaq6U1VvBE4GfgIUAN9V1bO9f+9S1fmpErQnxpQVUF6US24owIyqkoO+zqSKIoIBoTgvxKjS/AGUMHlyQwEEOG5SWVrqTxc5QdcEp1QWpVkSfynICTJ1ZDEBEWZVl2aU3jNHlxIKCKOH5VNRnJducVLG8ZPLCYgwsjSPqZU9vrt+wMjW/p0ODmU8nz1mGAERpo4spiAn6JOE/nDCpHJCQWFYQQ5HjBmWbnEMjz5fq+PFX/1WRB4DThWRk1X1nwAikqeqaZv6FuaGuOSkiagqh5IrderIYq6cP5mgCKFgepLgjy0r4FefmkdBrl+vk8xMyoty+ezpU8gfZANafxERzjuqek9bzSS9p40q4bCRxYfUhwYjC46s5qwZI8kNBQkE/NU9W/t3OjiU8XzuxHKOnVA2KPvCqdMqmTepjFAgkLbnmLE/fd4JEXlbRBaraqOqLgK+4x2/APiW3wLGWbyhniferGF3W5cv188LBdPaMNu7ojyzspba5o60yZAuMsHQSAUrtjXx2LIaahpd/KBfeneEozy9YgfPr65NOsYqE1MNqyovrd3FX9/aTmunP0s3qTCwILv7dzo4lPH8YPtC9/4di6W2U7V2RnhhzS5eXV+P5Q7PHJKZVm1Q1XcnfG4VkSAuP9b7/BFrX3Y0dfDi27sAiMSU846qBlycy4Ovbaa1K8qCI0czYUT6l14OltrmTu5/eSMra5q4+r0z0i2OMcC0d0X48bNv09DaxRtbdvta19LNu3lzayMAFcV5zO5j6WDV9iaeemsHFcV5fPDYseSGMmMWvH5XK694MWu5wQBnzBg5oNdPpd7WvwcHr66v56V1u5hUUcT7j6pO2qPVEY7y1IrtqEJ9WxcNbV38+Nm3mT+tkmPGp2aJ+JX1dXtik6uG5TPZ5yVwIzmSGVVURGaJyCkiEh/lLgYeUdWdAy2QKtS17LsCWZQXIiCuIY8oyt1zfGtDOw1tYboiMdbsSP8urUMhGlPauiI0tg+uYEsjOSIxpak9TFtXhN2t/nhj45QV5dIZiRKJxhhemLPPd41tYdq79t1Jt3p7M9GYsqOpg10tmRP4XlqQgyp0hqOUJfT7gWL19mZaOiNsbWjzXe9s7d89jefJ4n6v1O6SW7GtEVV4Z2cr7f3YcZoTDFCS7/paeWEuXZEYrZ0RVtY0+yXqfpQV5tIZjqIKwwpy+j7BSAkHSuGQIyIfBUqAmcBFuAD4ecAo4EcHOPd4EXlBRP4uIvNF5AkRuUNE+kxeWtPYzsIHlvD4sq17jgVF3Kz2nTo0YfljXHkh1cPzKckPMau6NAl1M5euaJRVNU20dmbX1tvWzgi3/nUVu1uH9jJKQShIXUsH7+xsoTPi74M2NxggGoOY6p4Ae4A3N9fzud8s4Zo/LaWxbW87m1ZVzJKNdbR2hhlZ4m/ge3/udygg7GxuZ31dKzlJOpleXlfHvzc1JFVWRFixrZHNDW2U5PkbKxWOxlizo5nOcPqNrEgkxrOralm7w38DoKfxPBl2tXTyncdXcsOjb7G2tn9yxmIxXnx7Jyu2NfbrPIA5E8ooyA0yq7qUwn7EzwUDwseOG88Hjx3Le2dXEY4qK2uaUrpkKLEIjy7dyvOrtxMKZIY32jiwJ6sCeDcQUdWHgEZV/RCwGBgOfPEA504DzgFuA44GrlHVhar6al8C1bd2sWRjAzcuWrnn2Itv1/LE8hpW1DRxw+N7j+fnBPnwvPFcfupkqocX9HXpjCYcVcIxeGTptnSLklI21rVx9z/W8/G7+2wag5rNDS2srW2lrSvGc6t2+VrXtsZ2CnOD5IaC7Gjaa8zcuGgVr6yv48nl2/njkk17jt/0l5Us3dzIQ0u2sMyHpcxtu9t5+Z06mjrCbKxv476XNvCJe17r87xX3qnjxbfrWL61kd+9urnP8ouWbuNHT6/he0+s4qW1e3/jtq4Iv3llI3/tlvtIVTl2QjmTKopp8Xm7flc0RmdEeXRZ+vv3z15Yxy9eWMe3//IWW3e3Dfj1/72pgXv+sZ6tu9t6HM+T4YXVO3luVS2vrK/jD0nc+0R++8omfvLsWm5atJIVNf0ztI4cO5yF86dw9qyqfp0HUJAbZFx5IcGAAEpAhNYUpoG47L7X2dbYybKtzdzwlzdTVq9xYHo11VW1BrhMRP7meaDyRWQBIKr6dRH5h4jco6q1PZx7P4CIzMW9eucoERkBfF5VtxxIIAViyj5u4s5IlIAIijL49nz0jxTHSqadmCpdkRjbdg9tT1YM3OvVFRR/b/JRY4ezs7mTnGCA6QmpTRrbw15Qr1LbtHcJJ564MBpTtjW2M6eP629paGPp5kamjSrmsFEHTp3SGYny59e3EI4qG+taiave3NH3w6cgJ0hhbpBITClJYvmjPmFTzK7Wvfrd+9IGXljtIhvKinI4btIIAGZXD+P1jQ2MKy+kKkWpWzKhe8fH1nBUaWwPM2b4wF27uSPMbU+tJhxV3trWiOKWDJs7+uehL8oLUpgbIqZKaUH/vIzbmzqob+0iGBDqm7tctscU09YVZdX2JvJzAsCUlNTZFdu7ylPX4m9IgpE8ybReBdYA9wLX4T0vgLuBjwA/7ukkEZkGjAMuUtUmETkBuBS4uYeyVwBXAOQMG0lOQBhfvtczdd5RY3l5XQPrdrVw7RANGo0bj2OGZU+eIoBQUCjIDQ75vC7jyoo5fmIZ6+vbOHtmFY897l9dRXkhzj96zH7HF542hR8+s5qivBCXnjxpz/FbPjCb7z65iqmVxSw4cv/zuvPUWztobA+zbmcLkyqKDriLSxAveNjN7MuLc5k5upQvnDm1z3pOmz6SS3e7OLFPnjihz/IfPHYsbZ1RckMBzp2198mak7B7MHH5dEVNE8MLc2nuiFDb3Olrjry8UICivADnHD7KtzqS5fJTJvGHxZuZOKKIw0cPbL9LvN/BgJATFIryghw/sbxf1zn78FHUNnfS3BFJ6t4nMm1UCYs3NlCYG6S6LD0rHKqurTUlMZkYKO64eA6f+80blOaHuO/TJ6SsXuPAJGNkTQBexC3/3Qj83Dv+V5zRtR8iUgj8ALgM+CjwC6CcvQbaPqjqncCdANWHzdL50yo594h9px+3XHBEEqIOXsqLczlrxsj99B7qlBXm8r4jqjn1sIp0i+IruaEAt390Dlsb2pleVcJjN6VehvPnjOGwqhKK80P7LK8fNb6M319xYtLXKSvKobE9TGl+yFsa6Z3cUICL5o7do/f3S/N5cOFJSdf1sePHJ122MDfEwtP39xpcctIkKorzqSjJ3WenV7kXTJ8bClDkc0xWWVEul58yJSNiR0cPL+Cqs6b5cu3i/BDXnjuDN7c08q6ZI3lsRBHXLTic9/Rz+S0QCPDJEycelAzjygs5ftIIggGhNE0B4CUFIWZWlaZ0PD/lsFEs+/Z7UlafkRzJJCOdnvhZRP7bO75NRL7Uy2nXAiNxhtaLIvIIEAWu7Ku+qtJ8rj9vFmPTNANJF9mqd2VJHp8/Y2pW6D2yJJ+RJel5owC44Nwjxh6652LBkdVs293OqNL8pLa4p1vv3FCA/5izv4fupCkjGF9eyLDCHIp9NrJGFOVy4ZyxWdHODx89bI+HrMCLm00lx4wvY2RpPoU5QV92pSZDto7nxv70e2RR1ccS/u5x4VdVr2NfL9d9yV4/IML4EYX9FWvQY3obg4WcYGBQ56SLIyKMK09N27N2nlrGpHkjlN1vI47t8zQMwzAMw/ABM7IMwzAMwzB8wIwswzAMwzAMHzAjyzAMwzAMwwfMyDIMwzAMw/ABM7IMwzAMwzB8wIwswzAMwzAMHzAjyzAMwzAMwwfMyDIMwzAMw/ABM7IMwzAMwzB8wIwswzAMwzAMHzAjyzAMwzAMwwfMyDIMwzAMw/ABM7IMwzAMwzB8ICVGloiMEpEnReSPIjI8FXUahmEYhmGkk1R5sq4GrgF+AHziQAVbOyM8uHgzm+vbUiJYpmB6m97ZgOltemcD2aq3sT+pMrJGA28Cy4BJByrY0hlha0M7/1i7KyWCZQqmt+mdDZjepnc2kK16G/uTKiOrQ1UVCALF3b8UkStEZLGILG5vagCgenhBikTLDEIBAUzvbMH0Nr2zAdM7u/Q29kec7eNzJSJfBl4E8oAzVfWmA5TdCbQC2TIFqMDpOgfYhOk91DG9Te9sINP1jsvnF5mqt19k+v3ujYFqBxNUtbKnL1JlZA0DfgqUAFeo6o4+yi9W1bm+C5YBJOpqeg99TG/TO90ypYJM1zsVMmWi3n6R6fe7N1Iha8jPi8dR1Ubg4lTUZRiGYRiGkQlYnizDMAzDMAwfyFQj6850C5BC7uzl76GO6W16ZwOmd2bqnQqZMlFvv8j0+90bvsuakpgswzAMwzCMbCNTPVmGYRiGYRiDGjOyDMMwDMMwfCAluwv7QkRKgU/issEXAZuBO1V1Z1oF85ls0ltExgHvBZYCq3B6nwVsx/Qecnonkk3tPJFs0ltE5qrqYhGpBhYCRwFbcAmo0653N/k+BUSBu1S1bgDryKb7nfXjWrJkiifrLuBfwLXAl4HnGFzBcwdLNul9P7ADWAC8gXvN0i5M76GqdyLZ1M4TySa9L/P+/ylwIvA0UEXm6J0o399wxsEvBriObLrfg3JcE5FSEfmCiNwmIneIyDdEpMckogNFRniygFKgU1W7AESkE2hJr0j+ISITgC8AJwDfBmqAW4EhrTfwlKo+KiLzgeuAfFVtH+r3myzVO1vbeZbqPVFEZgH1QD7uQfuuDGrne+RT1VcBROTKAa4jq55jDM5x7S7gBzgjMAgc4/39H35VmCmerEuAS0TkfhG5CzgF+HyaZfKTH+Ju9Dycq7UcN7saynpfA1zo/X0RbuZTmgX3O1v1huxs55Cdet+A0y8MfBa4DajMoHa+Rz4RKRaRK4A/D3Ad2fQcG6zj2h5DWFXbScHEx1I4pAER+T/gbuA1QIFTgXer6sK0CmYYA0i2tvNs1dswMh0ROQ5nFB4NnA5cDzygqpt9q9OMrNQjIvnAp4EJOEt6MbBIVaNpFcwwBpBsbefZqrdhZDoi8jNV/ZyIPAx8F+dl/rSqftCvOjNluTDb+AxuPXgrLlhwIpnrXjWMgyVb23m26m0YmU48Nm+Xqr6iqk/gc2x6pgS+Zxu7gfnArwFJVaUiElLVSKrqyxSyVe8MYDdpaOcZwG580FtECrw4koM5Nw8Yq6rrBkqeVCEiFcB4VX3dh2uXAy3xYPVUIyJBIKbdlpRERICAqkZFJKCqMe94QFVjInIB8Jaqrk6D2IOZeGxeRESKgY8x8LF5+2DLhWlCRI5U1WUpqGcScJOqXiwi9wAzgUW49ejRwOWq+nJC+euA7ap6l9+y+UmyeovIa6o6T0Q+A1yF2wmWD6xQ1SvSI/3QIVXtPNMYCL1FZC7wOVW9zPv8OjBfVZt7KPsNoFFVf9LLtT4FXKSq5/byfS6QBzyMC+CuwQXvX+d93q6q/3co+hwsInIicLGqfl5EclQ1LCIvqepJPZTNB6px/f1E3AP1fFVt9L4/GThHVb/pff4asFJVH0mVPt3kvQT4CC5v1wjv3xqccf4vVb1ZRB4BpntlXgCGA2cC64FaVT3fu1Y5cCzwb1XdlWJVjF4wT1aaSJGBdTNwGjBFRJ7H5TSZi9v1dCUugVx3D0+X92/QkqTeKiKneuVPw+2Keg54HqgEZqVc8CFINhpYcGh6i8incYZNIVDttWGAqcBjzsnBA6qamIuok/37cvx6lcDNwDoRWYSbZNTgwkUKVPUMXLqJ84B24H9wOaSKccucFwO3ich4Vd10sHodAmHcBgKARSJyHtAmIjnAHNwGg7NxW/HfBg4DfgmcAZwXN7A8OnFejBKcV+NoYI5nyD2nqn9NgT4AiEgZbqx5xjs0xfv3fEKZKcB3gA8BHcDvgR8Bs70iv/fKjcZ5ZBYBPxSRM1V1p4jcjTM4H1fVm72y+x07gIzPq+rph6xsFmNG1hBGVb8pImOBm1X1UhH5DfCo9/UDwFMAIrISFz8CMB7oEpFP4Dw6Lar63hSLfkgkqzduEJ6KM752AY24jMWQXctbRgahqncDd4vI0TgDZwXwIvC/OG/Ufp6s3vAMkd8A/6uq3/OOvayqC7oV3QSsBY4H/gisA+7AGSLX4vpF0vUOFCJyIfB1oEpEGnAG18eAI4A/4IysSTgvz29U9VoReURVbxSRo1W1PuFaHwb+E6gAcnE5zBYCy3HG12lAyowsoAxn5MUNneNweizyPn8QmAbsBGLedzFcNvnLvTLl3v+zgC973vkynOFYBARV9SQR+ZmIHIb73fY5pqpv+6pllpO1RpbnHs9R1dZ0y+IXInIRbglskojcC/wMl3xNveNXeUXDqnqWd85XcUsDD4jIRKDH5YdMJkm9FwFvAa24GX0Ul/flZJxxOWQ8MF5b777JJXyg3W6DsX8M5rij7ohICFiqqm+IyDHAsYmTHRH5EPA19nqdRwNREbnU+5wP3AQsAV7GGSnxh/d0EXkcN/4/6i0xjsUF6I/B9YnTcFvdjwC+gus78bxIqeRhnAF4CS6f0Xycx3mtql7geVrUMybjv0Xc69UuIqWq2gSgqn8QkXXAAlX9NoCIxNhrwKQ6dqYL54VbiDO27sB5I+/AGbX343T+Du7+dAEzcEbWKd41hgOo6tOwxyt/HHAjznh70Cv3rHfOMd2PicjvgVrv+lXA71T19gHXdoAQ92qkXT3F0YnISFwerMb9z0wPWWFkeUGEwW7BzycC78LlyYiXex2XsXgXUICbSWwBVgK5PcUAZDh/wc2CN+CCcDtwmah/Cbwf1+l+iBtkhhLJ6n0h0IBbKqkBvud9fxHwTREJZvK2exE5E+dp6MTNzOODTi7wn6q61Pv8OK4Nx3U5HDfQ/tK7TlL9YxDwMdy92y/uKJNjjnrhNuAIzwgAQERuB0YB13peqQcTvvsqzut8Rw/Xul5EngXOVtWI58na5zdS1X948UrxQOx7gV95k5Rvq+qGAdUuSbzA77jx8yvgNFW9TUTO6VY0Hzd2J7IS1473806JyC3AkcBkoM37N0JEGlX1RwOpQ2+o6hbgf0Tk47iktVtwhs8qr8jfgJHA73CGZk9jkYjI4aq6wuvHH8Z5+6I4Yyy+QtGE89r3dKyQvQlFzwK+OYBq+sEtuN/mtz1891mcgbrnlUkislpVp6dItv3ICiMLGAf8WkQiuAfMpTgPzSYReRqYqqoTgWXe8tJ9uODDY3Bu8wfwMe2+j8RwXpxbcC/t/DnuoXwGzri4xysX8n4H2LtceClu4GpKpcADRDJ6C85dvwM3U4znSVmPexC/kMkGFoCqPosblPECgU/rpWgnbjkhhkuS+Ua375PtHxlLX3FHwLfI3Jij/VDVL8X/FpdA8Wu4Je9bVXVNstcRt3sNevHSiEgA1xdCuNjFNcAZIvJJ3MN6OnCviISBX6vq/QehzoCgqrUJBhfeGFXmfZwGvOMtkXV4x57DxTJ1N7KqcEHl3xCRP3mfz1HVNj/l7wkR+SjOyO/C/f5xinDt9jHv81hcG76ZfWNmA3ixeN4Oxc+LyE3etVq8a4Br64Feju1Q1RYR2YgzzjIuVEJEVuPGcnDyHy4i8fdRTlTVqV5bPwe3uSmRhtRI2TNZYWSp6iYRWeA1pAdxjfRO3Czmv4D4DHaa91DZoqrXA4jIDtzS0qCKSwJQ1S4ReQrnfn4vLvnaVbgYhL/hZst/Bi7Tve/zSlwuzMcNXoOKJPVuwBleN6vqEhGZiesP9+NeIntbGkQ/FPYLehaRKtxMdTjOYzMLtxwa32l1CvByP/pHRpJM3JG39J1xMUcHQkROwnkSNwFXHaQBeAVwPs64fNg5O5iesHQYxN3fctzE5OM4I/v9Xr0bcROvE+jZc5AKcoGjROT7CcfycPLFJ7+zgYdwnp86cZsH/gycJiLVqrrNW2L9Cu7l0Gu8mLcO3G98O+63SjXx+NBwt+NHA8NUdY2IbMB5nUO4TTtxCoDvqeqLIvL/gBpV/TWuv+/GLRWfglsuPgpYjfOWdT82GIjgDOZpXtzZh3C7Qt/0VqDAefEALvdCRuIG+eHiNo/8RFUfSqnUZImR5fFjb2CpTTh2H+7GxGd7azxP1rPejfsYrvP+BChJqbQDRy1uQHofbhmtCTdYjsY9bJarl4PFIz6zRVU7GLyxSX3pfZ+6fDPxPjAMN/urxs18HxGR/1LVtSmXPAlEZBjO49bpHZrtLQnFd2IV4/SoxgVMj8QF0O5MuMyduKD/CMn1j0xlDH3EHeG8dxPJvJijHhGR63FLNyXA33HvAWzAyZ6DW+JpBaLdPa7ejD6gqmFV/TnOk5v4/SvdA99FJE9VO0XkYu/Ql3CTy5247PWv4bwEi0ghInIucDUuyP3PwM9FZDwuBctqEfmAiHwBmKmqa0Xk/Tgj4yLv/+8Dv/eWF58DHlPVVi/O7SfABZ6H7GQR+SXwlRTH88RwHteObsfHAsNFZA7O+N0FfF1VH4gXEJHLcasN4Pryg96x5TjjrQR40YthOgdnKGsPxwZDqppzcGP3hSKyAPcOwu0AqjpHRIYDX8T9jj8Hfuk9v+ITrtMTvLopJZuMrIXAK7hYnABudiY4a/4eERmXUHYT8JL39xLcQ2pMJi0nJIO4l6CeifNmfArXWYtxMwLBuaQLcZ6bOMW4meOgpZ96l3qnjcG9/uS/1eXh+QwZHKvmPQjOgD2BoLfjDKRb4/EzIjIVp8Od7NvXi4BXcQ/S+Ay6z/6hPr7f61Dw9O0z7igTY456wjMiZuPubx5uKfuLOM/icNy9/AbOQH5Xt7itS3H370Hg1l6qKO5+QFU7E747HOfxfQhnjN6B2yQy8VD0OhhU9XFcTCEAIvIAzvj3QpBoA57A887iJhMrcROpz3rxW3NxubJ+610jB+fZ/oiq1nr13CgiX8JNylLpsZuPiyPq7smqxN2DjcBlOO/TNzwjKs5oXKgDqtoAvLvbNZpE5HTv+A90b66w7sdO965xunfepfELaOakbyjCeeN/i5s0P4hb2o5zKq5PXOvF8d0gIotV9eF4gXSFf2RNMlLPNXwDzsV6JS5pW5wjce70D+PcrBtwbtUHcNt8a3Ezi4/0tKMhUxGRAqBDe7jJnlWv3bxYQ4Js01tEPo8bpI8kwchK+H6fBxVucC5S1asSyhxNH/1DVb/rg/iHTMIM9SngPQlG1gne9wGc9+dpXD+vwcWhxWOONnh/pzXmyDg4PGsrXw8yG/5gwDMMVbPwzRWe5+pqXN8N4CaO83DeVXAG2Ne9ZdOnVfUsEZkO3KaqCxLHgnSQFZ4sEZmNC3a9ADeYPoC7MYozqn6Ly7Rbo6rneLNbvDLH4wKop+GWXjakUPRD4kCDTqYHdR8K2aS358VaiIu1OjLh+Fhce47n1vlAwmlFOK9VvGyy/SNTSSbu6F9kbsyRcQh4k6kha2ABqGp3T1fWoKqL8JapPYP6O7iErTtwSXn3+23iS8kpFLNXssLIwnXAT6pqnff5/fEvxOXVuBq35r0m7oPGzeiPBq5W1YdE5G+4m2oYGYG4LM+LgBtUtclbNqrETQRuwC33PIGLz4nnQQvhst9/JOFSyfSPp8lQkok7ytSYI8Mw+kZExgDvwfXVx3DxdFcBL4nI91T1T17RPfkAE7x+abVzuicoHJKo6jpV3djL11fhdlOV4gKJH8dtcz0c57n6ooj8E/gTbu3cMNKOF7fzd+CWhB0zDwO3isg/cMbW897xxBi7ebiA+LirPdn+8exAyZ4i9ok7OkDMkeJijrLtJdaGMSgQl2T4Otwz+kJV/Y6qtqh7JdACYJ4XIgIuLCB+Xom4t5k8l3KhE8iamKy+EBFJjOERkfz47gTvc0YnpjSyD0nIZm0YhmFkHmZkGYZhGIZh+EBWLBcahmEYhmGkGjOyDMMwDMMwfMCMLMMwDMMwDB8wI8swDMMwDMMHzMgyDMMwDMPwgf8P0x3lQPnVDAwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 81 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from pandas.plotting import scatter_matrix\n",
    "font = {\n",
    "    'family':'SimHei'\n",
    "}\n",
    "matplotlib.rc('font',**font)\n",
    "\n",
    "scatter_matrix(\n",
    "    data[[ \"鸡腿\",\"翅中\",\"翅根\",\"无骨鸡\",\"年糕\",\"芝士年糕\",\"地瓜块\",\"雪碧300ml\",\"实付\"]],\n",
    "    figsize =(10,10),diagonal = 'kid'\n",
    ")\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6348eb33",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>鸡腿</th>\n",
       "      <th>翅中</th>\n",
       "      <th>翅根</th>\n",
       "      <th>无骨鸡</th>\n",
       "      <th>年糕</th>\n",
       "      <th>芝士年糕</th>\n",
       "      <th>地瓜块</th>\n",
       "      <th>雪碧300ml</th>\n",
       "      <th>实付</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>鸡腿</th>\n",
       "      <td>7.433333</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>-1.033333</td>\n",
       "      <td>-4.900000</td>\n",
       "      <td>5.300000</td>\n",
       "      <td>-1.116667</td>\n",
       "      <td>3.366667</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>31.172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>翅中</th>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.304762</td>\n",
       "      <td>0.323810</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>2.245286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>翅根</th>\n",
       "      <td>-1.033333</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>1.047619</td>\n",
       "      <td>1.057143</td>\n",
       "      <td>-0.385714</td>\n",
       "      <td>0.152381</td>\n",
       "      <td>0.361905</td>\n",
       "      <td>-0.042857</td>\n",
       "      <td>-0.572857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无骨鸡</th>\n",
       "      <td>-4.900000</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>1.057143</td>\n",
       "      <td>8.328571</td>\n",
       "      <td>-1.592857</td>\n",
       "      <td>2.542857</td>\n",
       "      <td>-0.585714</td>\n",
       "      <td>-0.471429</td>\n",
       "      <td>-6.302429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年糕</th>\n",
       "      <td>5.300000</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>-0.385714</td>\n",
       "      <td>-1.592857</td>\n",
       "      <td>8.414286</td>\n",
       "      <td>-0.414286</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>26.455143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芝士年糕</th>\n",
       "      <td>-1.116667</td>\n",
       "      <td>0.304762</td>\n",
       "      <td>0.152381</td>\n",
       "      <td>2.542857</td>\n",
       "      <td>-0.414286</td>\n",
       "      <td>3.247619</td>\n",
       "      <td>-1.561905</td>\n",
       "      <td>-0.157143</td>\n",
       "      <td>0.352857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地瓜块</th>\n",
       "      <td>3.366667</td>\n",
       "      <td>0.323810</td>\n",
       "      <td>0.361905</td>\n",
       "      <td>-0.585714</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>-1.561905</td>\n",
       "      <td>6.990476</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>20.199286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雪碧300ml</th>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>-0.042857</td>\n",
       "      <td>-0.471429</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>-0.157143</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>6.862071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>实付</th>\n",
       "      <td>31.172000</td>\n",
       "      <td>2.245286</td>\n",
       "      <td>-0.572857</td>\n",
       "      <td>-6.302429</td>\n",
       "      <td>26.455143</td>\n",
       "      <td>0.352857</td>\n",
       "      <td>20.199286</td>\n",
       "      <td>6.862071</td>\n",
       "      <td>189.570171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                鸡腿        翅中        翅根       无骨鸡         年糕      芝士年糕  \\\n",
       "鸡腿        7.433333  0.033333 -1.033333 -4.900000   5.300000 -1.116667   \n",
       "翅中        0.033333  0.190476  0.095238  0.514286   0.028571  0.304762   \n",
       "翅根       -1.033333  0.095238  1.047619  1.057143  -0.385714  0.152381   \n",
       "无骨鸡      -4.900000  0.514286  1.057143  8.328571  -1.592857  2.542857   \n",
       "年糕        5.300000  0.028571 -0.385714 -1.592857   8.414286 -0.414286   \n",
       "芝士年糕     -1.116667  0.304762  0.152381  2.542857  -0.414286  3.247619   \n",
       "地瓜块       3.366667  0.323810  0.361905 -0.585714   2.428571 -1.561905   \n",
       "雪碧300ml   1.150000  0.114286 -0.042857 -0.471429   0.257143 -0.157143   \n",
       "实付       31.172000  2.245286 -0.572857 -6.302429  26.455143  0.352857   \n",
       "\n",
       "               地瓜块   雪碧300ml          实付  \n",
       "鸡腿        3.366667  1.150000   31.172000  \n",
       "翅中        0.323810  0.114286    2.245286  \n",
       "翅根        0.361905 -0.042857   -0.572857  \n",
       "无骨鸡      -0.585714 -0.471429   -6.302429  \n",
       "年糕        2.428571  0.257143   26.455143  \n",
       "芝士年糕     -1.561905 -0.157143    0.352857  \n",
       "地瓜块       6.990476  0.714286   20.199286  \n",
       "雪碧300ml   0.714286  0.428571    6.862071  \n",
       "实付       20.199286  6.862071  189.570171  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cov(协方差矩阵)\n",
    "data[[ \"鸡腿\",\"翅中\",\"翅根\",\"无骨鸡\",\"年糕\",\"芝士年糕\",\"地瓜块\",\"雪碧300ml\",\"实付\"]].cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07534071",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>鸡腿</th>\n",
       "      <th>翅中</th>\n",
       "      <th>翅根</th>\n",
       "      <th>无骨鸡</th>\n",
       "      <th>年糕</th>\n",
       "      <th>芝士年糕</th>\n",
       "      <th>地瓜块</th>\n",
       "      <th>雪碧300ml</th>\n",
       "      <th>实付</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>鸡腿</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028013</td>\n",
       "      <td>-0.370294</td>\n",
       "      <td>-0.622758</td>\n",
       "      <td>0.670155</td>\n",
       "      <td>-0.227274</td>\n",
       "      <td>0.467041</td>\n",
       "      <td>0.644309</td>\n",
       "      <td>0.830401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>翅中</th>\n",
       "      <td>0.028013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213201</td>\n",
       "      <td>0.408318</td>\n",
       "      <td>0.022569</td>\n",
       "      <td>0.387488</td>\n",
       "      <td>0.280618</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.373651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>翅根</th>\n",
       "      <td>-0.370294</td>\n",
       "      <td>0.213201</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.357888</td>\n",
       "      <td>-0.129914</td>\n",
       "      <td>0.082613</td>\n",
       "      <td>0.133733</td>\n",
       "      <td>-0.063960</td>\n",
       "      <td>-0.040650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无骨鸡</th>\n",
       "      <td>-0.622758</td>\n",
       "      <td>0.408318</td>\n",
       "      <td>0.357888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190276</td>\n",
       "      <td>0.488938</td>\n",
       "      <td>-0.076762</td>\n",
       "      <td>-0.249528</td>\n",
       "      <td>-0.158613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年糕</th>\n",
       "      <td>0.670155</td>\n",
       "      <td>0.022569</td>\n",
       "      <td>-0.129914</td>\n",
       "      <td>-0.190276</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079252</td>\n",
       "      <td>0.316657</td>\n",
       "      <td>0.135411</td>\n",
       "      <td>0.662394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芝士年糕</th>\n",
       "      <td>-0.227274</td>\n",
       "      <td>0.387488</td>\n",
       "      <td>0.082613</td>\n",
       "      <td>0.488938</td>\n",
       "      <td>-0.079252</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.327807</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>0.014221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地瓜块</th>\n",
       "      <td>0.467041</td>\n",
       "      <td>0.280618</td>\n",
       "      <td>0.133733</td>\n",
       "      <td>-0.076762</td>\n",
       "      <td>0.316657</td>\n",
       "      <td>-0.327807</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412674</td>\n",
       "      <td>0.554878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>雪碧300ml</th>\n",
       "      <td>0.644309</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.063960</td>\n",
       "      <td>-0.249528</td>\n",
       "      <td>0.135411</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>0.412674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.761305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>实付</th>\n",
       "      <td>0.830401</td>\n",
       "      <td>0.373651</td>\n",
       "      <td>-0.040650</td>\n",
       "      <td>-0.158613</td>\n",
       "      <td>0.662394</td>\n",
       "      <td>0.014221</td>\n",
       "      <td>0.554878</td>\n",
       "      <td>0.761305</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               鸡腿        翅中        翅根       无骨鸡        年糕      芝士年糕       地瓜块  \\\n",
       "鸡腿       1.000000  0.028013 -0.370294 -0.622758  0.670155 -0.227274  0.467041   \n",
       "翅中       0.028013  1.000000  0.213201  0.408318  0.022569  0.387488  0.280618   \n",
       "翅根      -0.370294  0.213201  1.000000  0.357888 -0.129914  0.082613  0.133733   \n",
       "无骨鸡     -0.622758  0.408318  0.357888  1.000000 -0.190276  0.488938 -0.076762   \n",
       "年糕       0.670155  0.022569 -0.129914 -0.190276  1.000000 -0.079252  0.316657   \n",
       "芝士年糕    -0.227274  0.387488  0.082613  0.488938 -0.079252  1.000000 -0.327807   \n",
       "地瓜块      0.467041  0.280618  0.133733 -0.076762  0.316657 -0.327807  1.000000   \n",
       "雪碧300ml  0.644309  0.400000 -0.063960 -0.249528  0.135411 -0.133199  0.412674   \n",
       "实付       0.830401  0.373651 -0.040650 -0.158613  0.662394  0.014221  0.554878   \n",
       "\n",
       "          雪碧300ml        实付  \n",
       "鸡腿       0.644309  0.830401  \n",
       "翅中       0.400000  0.373651  \n",
       "翅根      -0.063960 -0.040650  \n",
       "无骨鸡     -0.249528 -0.158613  \n",
       "年糕       0.135411  0.662394  \n",
       "芝士年糕    -0.133199  0.014221  \n",
       "地瓜块      0.412674  0.554878  \n",
       "雪碧300ml  1.000000  0.761305  \n",
       "实付       0.761305  1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr(相关矩阵) \n",
    "data[[ \"鸡腿\",\"翅中\",\"翅根\",\"无骨鸡\",\"年糕\",\"芝士年糕\",\"地瓜块\",\"雪碧300ml\",\"实付\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ccf8de0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.82880491 0.02849099 2.05067398 1.6464831  0.90137357 0.57539691\n",
      "  0.16108292 7.14782456]]\n",
      "[3.3500007]\n"
     ]
    }
   ],
   "source": [
    "x = data[[\"鸡腿\",\"翅中\",\"翅根\",\"无骨鸡\",\"年糕\",\"芝士年糕\",\"地瓜块\",\"雪碧300ml\"]]\n",
    "y = data[[\"实付\"]] \n",
    "\n",
    "#建模\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lrModel = LinearRegression()\n",
    "\n",
    "#训练模型\n",
    "lrModel.fit(x,y)\n",
    "\n",
    "#查看参数\n",
    "print(lrModel.coef_)\n",
    "\n",
    "#查看截距\n",
    "print(lrModel.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c70018a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.57443857]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrModel.predict([[0, 0, 0, 8, 6, 0, 4, 0]])"
   ]
  }
 ],
 "metadata": {
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